text:
\section{Synonyms}

Microblog sentiment analysis; Twitter opinion mining

\section{Glossary}

	\textbf{Sentiment Analysis:} This is text analysis aiming to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a piece of text.

\section{Definition}

Sentiment analysis on Twitter is the use of natural language processing techniques to identify and categorize opinions expressed in a tweet, in order to determine the author's attitude toward a particular topic or in general. Typically, discrete labels such as \emph{positive}, \emph{negative}, \emph{neutral}, and \emph{objective} are used for this purpose, but it is also possible to use labels on an ordinal scale, or even continuous numerical values.

\section{Introduction}

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world.
This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before.

Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: \emph{Do people like the new Apple Watch? What do they hate about iPhone6? Do Americans support ObamaCare? What do Europeans think of Pope's visit to Palestine? How do we recognize the emergence of health problems such as depression? Do Germans like how Angela Merkel is handling the refugee crisis in Europe? What do republican voters in USA like/hate about Donald Trump? How do Scottish feel about the Brexit?}

Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages~\cite{java2007we,kwak2010twitter}.

Despite all these opportunities, the rise of social media has also
presented new challenges for natural language processing (NLP) applications,
which had largely relied on NLP tools tuned for formal text genres such as newswire,
and thus were not readily applicable to the informal language and style of social media.
That language proved to be quite challenging
with its use of creative spelling and punctuation, misspellings, slang, new words, URLs, and genre-specific terminology and abbreviations, e.g., RT for re-tweet and \#hashtags.
In addition to the genre difference, there is also a difference in length:
social media messages are generally short, often length-limited by design as in Twitter,
i.e., a sentence or a headline rather than a full document.
How to handle such challenges has only recently been the subject of thorough research
\cite{Barbosa10,Bifet11,Davidov:2010:SRS,Jansen09,Kouloumpis11,oconnor10,Pak10,Tumasjan10}.

\section{Key Points}

Sentiment analysis has a wide number of applications
in areas such as market research, political and social sciences,
and for studying public opinion in general,
and Twitter is one of the most commonly-used platforms for this.
This is due to its streaming nature, which allows for real-time analysis,
to its social aspect, which encourages people to share opinions, and
to the short size of the tweets, which simplifies linguistic analysis.

\noindent There are several formulations of the task of Sentiment Analysis on Twitter
that look at different sizes of the target (e.g., at the level of words vs. phrases vs. tweets vs. sets of tweets),
at different types of semantic targets (e.g., aspect vs. topic vs. overall tweet),
at the explicitness of the target (e.g., sentiment vs. stance detection),
at the scale of the expected label (2-point vs. 3-point vs. ordinal), etc.
All these are explored at SemEval, the International Workshop on Semantic Evaluation, which has created a number of benchmark datasets and has enabled direct comparison between different systems and approaches, both as part of the competition and beyond.

Traditionally, the task has been addressed using supervised and semi-supervised methods, as well as using distant supervision,
with the most important resource being sentiment polarity lexicons,
and with feature-rich approaches as the dominant research direction for years.
With the recent rise of deep learning, which in many cases eliminates the need for any explicit feature modeling, the importance of both lexicons and features diminishes, while at the same time attention is shifting towards learning from large unlabeled data, which is needed to train the high number of parameters of such complex models.
Finally, as methods for sentiment analysis mature, more attention is also being paid to linguistic structure and to multi-linguality and cross-linguality.

\section{Historical Background}

Sentiment analysis emerged as a popular research direction in the early 2000s.
Initially, it was regarded as standard document classification into topics such as business, sport, and politics \cite{Pang:2002:TUS}.
However, researchers soon realized that it was quite different from standard document classification \cite{Sebastiani:2002:MLA:survey},
and that it crucially needed external knowledge in the form of sentiment polarity lexicons.

Around the same time, other researchers realized the importance of external sentiment lexicons, e.g., Turney~\cite{turney2002thumbs} proposed an unsupervised approach to learn the sentiment orientation of words/phrases: positive vs. negative. Later work studied the linguistic aspects of expressing opinions, evaluations, and speculations \cite{Wiebe:2004:LSL}, the role of context in determining the sentiment orientation \cite{Wilson05}, of deeper linguistic processing such as negation handling \cite{PangL08}, of finer-grained sentiment distinctions \cite{Pang2005},
of positional information \cite{raychev-nakov:2009:RANLP09},
etc. Moreover, it was recognized that in many cases, it is crucial to know not just the polarity of the sentiment but also the topic toward which this sentiment is expressed \cite{Stoyanov:2008:TIF}.

Until the rise of social media, research on opinion mining and sentiment analysis
had focused primarily on learning about the language of sentiment in general,
meaning that it was either genre-agnostic \cite{swn}
or focused on newswire texts \cite{Wiebe05} and customer reviews (e.g., from web forums),
most notably about movies \cite{Pang:2002:TUS} and restaurants \cite{Semeval2014task4}
but also about hotels, digital cameras, cell phones, MP3 and DVD players \cite{Hu04},
laptops \cite{Semeval2014task4},
etc.
This has given rise to several resources, mostly word and phrase polarity lexicons,
which have proven to be very valuable for their respective domains and types of texts,
but less useful for short social media messages.

\noindent Later, with the emergence of social media, sentiment analysis in Twitter became a hot research topic.
Unfortunately, research in that direction was hindered by the unavailability of suitable datasets and lexicons for system training, development, and testing.
While some Twitter-specific resources were developed,
initially they were either small and proprietary, such as the i-sieve corpus \cite{Kouloumpis11},
were created only for Spanish like the TASS corpus \cite{TASS},
or relied on noisy labels obtained automatically, e.g.,
based on emoticons and hashtags
\cite{mohammad:2012:STARSEM-SEMEVAL,MohammadKZ2013,Pang:2002:TUS}.

This situation changed with the shared task on \emph{Sentiment Analysis on Twitter}, which was organized at SemEval, the International Workshop on Semantic Evaluation, a semantic evaluation forum previously known as SensEval. The task ran in 2013, 2014, 2015, and 2016, attracting over 40 participating teams in all four editions. While the focus was on general tweets, the task also featured out-of-domain testing on SMS messages, LiveJournal messages, as well as on sarcastic tweets.

SemEval-2013 Task 2 \cite{Semeval2013} and SemEval-2014 Task 9 \cite{rosenthal-EtAl:2014:SemEval} focused on expression-level and message-level polarity.
SemEval-2015 Task 10 \cite{Nakov:2016rm,Rosenthal-EtAl:2015:SemEval} featured topic-based message polarity classification on detecting trends toward a topic and on determining the out-of-context (a priori) strength of association of Twitter terms with positive sentiment.
SemEval-2016 Task 4 \cite{nakov-EtAl:2016:SemEval} introduced a 5-point scale, which is used for human review ratings on popular websites such as Amazon, TripAdvisor, Yelp, etc.; from a research perspective, this meant moving from classification to \emph{ordinal regression}.
Moreover, it focused on \emph{quantification}, i.e., determining what proportion of a set of tweets on a given topic are positive/negative about it. It also featured a 5-point scale \emph{ordinal quantification} subtask \cite{Gao:2015ly}.

Other related
tasks have explored
aspect-based sentiment analysis \cite{pontiki-EtAl:2015:SemEval,SemEval:2016:task5,Semeval2014task4},
sentiment analysis of figurative language on Twitter \cite{ghosh-EtAl:2015:SemEval},
implicit event polarity \cite{russo-caselli-strapparava:2015:SemEval},
stance in tweets \cite{SemEval:2016:task6},
out-of-context sentiment intensity of phrases \cite{SemEval:2016:task7},
and emotion detection \cite{SemEval2007}.
Some of these tasks featured languages other than English.

\section{Sentiment Analysis on Twitter: A SemEval Perspective}

\subsection{Variants of the Task at SemEval}

\textbf{Tweet-level sentiment.} The simplest and also the most popular task of sentiment analysis on Twitter is to determine the overall sentiment expressed by the author of a tweet \cite{nakov-EtAl:2016:SemEval,Nakov:2016rm,Semeval2013,Rosenthal-EtAl:2015:SemEval,rosenthal-EtAl:2014:SemEval}.
Typically, this means choosing one of the following three classes to describe the sentiment: \textsc{Positive}, \textsc{Negative}, and \textsc{Neutral}. Here are some examples:

\begin{enumerate}
	\item[(1)]  \textsc{Positive}: \textit{@nokia lumia620 cute and small and pocket-size, and available in the brigh
	 test colours of day! \#lumiacaption}
	\item[(2)]  \textsc{Negative}: \textit{I hate tweeting on my iPhone 5 it's so small :(}
	\item[(3)] \textsc{Neutral}: \textit{If you work as a security in a samsung store...Does that make you guardian of the galaxy??}
\end{enumerate}

\textbf{Sentiment polarity lexicons.} Naturally, the overall sentiment in a tweet can be determined based on the sentiment-bearing words and phrases it contains as well as based on emoticons such as \texttt{;)} and\texttt{:(}. For this purpose, researchers have been using lexicons of sentiment-bearing words.
For example, \emph{cute} is a positive word, while \emph{hate} is a negative one, and the occurrence of these words in (1) and (2) can help determine the overall polarity of the respective tweet. We will discuss these lexicons in more detail below.

\textbf{Prior sentiment polarity of multi-word phrases.} Unfortunately, many sentiment-bearing words are not universally good or universally bad. For example, the polarity of an adjective could depend on the noun it modifies, e.g., \emph{hot coffee} and \emph{unpredictable story} express positive sentiment, while \emph{hot beer} and \emph{unpredictable steering} are negative. Thus, determining the out-of-context (a priori) strength of association of Twitter terms, especially multi-word terms, with positive/negative sentiment is an active research direction \cite{Nakov:2016rm,Rosenthal-EtAl:2015:SemEval}.

\textbf{Phrase-level polarity in context.} Even when the target noun is the same, the polarity of the modifying adjective could be different in different tweets, e.g., \emph{small} is positive in (1) but negative in (2), even though they both refer to a phone. Thus, there has been research in determining the sentiment polarity of a term in the context of a tweet \cite{Semeval2013,Rosenthal-EtAl:2015:SemEval,rosenthal-EtAl:2014:SemEval}.

\textbf{Sarcasm.} Going back to tweet-level sentiment analysis, we should mention sarcastic tweets, which are particularly challenging as the sentiment they express is often the opposite of what the words they contain suggest \cite{Davidov:2010:SRS,Rosenthal-EtAl:2015:SemEval,rosenthal-EtAl:2014:SemEval}. For example, (4) and (5) express a negative sentiment even though they contain positive words and phrases such as \emph{thanks}, \emph{love}, and \emph{boosts my morale}.

\begin{enumerate}
	\item[(4)]  \textsc{Negative}: \textit{Thanks manager for putting me on the schedule for Sunday}
	\item[(5)]  \textsc{Negative}: \textit{I just love missing my train every single day. Really boosts my morale.}
\end{enumerate}

\textbf{Sentiment toward a topic.} Even though tweets are short, as they are limited to 140 characters by design (even though this was relaxed a bit as of September 19, 2016, and now media attachments such as images, videos, polls, etc., and quoted tweets no longer reduce the character count),
they are still long enough to allow the tweet's author to mention several topics and to express potentially different sentiment toward each of them.
A topic can be anything that people express opinions about, e.g., a product (e.g., \emph{iPhone6}), a political candidate (e.g., \emph{Donald Trump}), a policy (e.g.,~\emph{Obamacare}), an event (e.g.,~\emph{Brexit}), etc.
For example, in (6) the author is positive about Donald Trump but negative about Hillary Clinton. A political analyzer would not be interested so much in the overall sentiment expressed in the tweet (even though one could argue that here it is positive overall), but rather in the sentiment with respect to a topic of his/her interest of study.

\begin{enumerate}
	\item[(6)] \textit{As a democrat I couldnt ethically support Hillary no matter who was running against her. Just so glad that its Trump, just love the guy!}\\
	 (topic: \emph{Hillary} $\rightarrow$ \textsc{Negative})\\
	 (topic: \emph{Trump} $\rightarrow$ \textsc{Positive})
\end{enumerate}

\textbf{Aspect-based sentiment analysis.} Looking again at (1) and (2), we can say that the sentiment is not about the phone (\emph{lumia620} and \emph{iPhone 5}, respectively), but rather about some specific aspect thereof, namely, \textsc{size}. Similarly, in (7) instead of sentiment toward the topic \emph{lasagna}, we can see sentiment toward two aspects thereof: \textsc{quality} (\textsc{Positive} sentiment) and \textsc{quantity} (\textsc{Negative} sentiment). Aspect-based sentiment analysis is an active research area \cite{pontiki-EtAl:2015:SemEval,SemEval:2016:task5,Semeval2014task4}.

\begin{enumerate}
	\item[(7)] \textit{The lasagna is delicious but do not come here on an empty stomach.}
\end{enumerate}

\textbf{Stance detection.} A task related to, but arguably different in some respect from sentiment analysis, is that of \emph{stance detection}. The goal here is to determine whether the author of a piece of text is in favor of, against, or neutral toward a proposition or a target \cite{SemEval:2016:task6}.
For example, in (8) the author has a negative stance toward the proposition \emph{women have the right to abortion}, even though the target is not mentioned at all. Similarly, in (9§) the author expresses a negative sentiment toward \emph{Mitt Romney}, from which one can imply that s/he has a positive stance toward the target \emph{Barack Obama}.

\begin{enumerate}
	\item[(8)] \textit{A foetus has rights too! Make your voice heard.}\\
	(Target: \emph{women have the right to abortion} $\rightarrow$ \textsc{Against})
\end{enumerate}

\begin{enumerate}
	\item[(9)] \textit{All Mitt Romney cares about is making money for the rich.}\\
	(Target: \emph{Barack Obama} $\rightarrow$ \textsc{InFavor})
\end{enumerate}

\textbf{Ordinal regression.} The above tasks were offered in different granularities, e.g., 2-way (\textsc{Positive}, \textsc{Negative}), 3-way (\textsc{Positive}, \textsc{Neutral},
\textsc{Negative}), 4-way (\textsc{Positive}, \textsc{Neutral}, \textsc{Negative}, \textsc{Objective}), 5-way (\textsc{HighlyPositive}, \textsc{Positive}, \textsc{Neutral}, \textsc{Negative}, \textsc{HighlyNegative}), and sometimes even 11-way \cite{ghosh-EtAl:2015:SemEval}. It is important to note that the 5-way and the 11-way scales are ordinal, i.e., the classes can be associated with numbers, e.g., $-$2, $-$1, 0, 1, and 2 for the 5-point scale. This changes the machine learning task as not all mistakes are equal anymore \cite{Pang2005}. For example, misclassifying a \textsc{HighlyNegative} example as \textsc{HighlyPositive} is a bigger mistake than misclassifying it as \textsc{Negative} or as \textsc{Neutral}. From a machine learning perspective, this means moving from \emph{classification} to \emph{ordinal regression}. This also requires different evaluation measures \cite{nakov-EtAl:2016:SemEval}.

\textbf{Quantification.}
Practical applications are hardly ever interested in the sentiment expressed in a \emph{specific tweet}. Rather, they look at estimating the \textit{prevalence} of positive
  and negative tweets about a given topic in a set of tweets from some time interval.
Most (if not all) tweet sentiment classification studies conducted within political science
  \cite{Borge-Holthoefer:2015dz,Kaya:2013ca,marchettibowick-chambers:2012:EACL2012},
  economics \cite{Bollen:2011bf,oconnor10}, social science
  \cite{Dodds:2011uq}, and market research
  \cite{Burton:2011sh,Qureshi:2013fb} use Twitter with an interest in
  aggregate data and \emph{not} in individual classifications.
  Thus, some tasks, such as SemEval-2016 Task 4 \cite{nakov-EtAl:2016:SemEval}, replace classification with class prevalence estimation, which is also known as \textit{quantification} in data mining and related fields. Note that quantification is not a mere byproduct of classification, since a good classifier is not necessarily a good quantifier, and vice versa \cite{Forman:2008kx}.
  Finally, in case of multiple labels on an ordinal scale, we have yet another machine learning problem: \emph{ordinal quantification}. Both versions of quantification require specific evaluation measures and machine learning algorithms.

\subsection{Features and Learning}

\textbf{Pre-processing.} Tweets are subject to standard preprocessing steps for text such as tokenization, stemming, lemmatization, stop-word removal, and part-of-speech tagging. Moreover, due to their noisy nature, they are also processed using some Twitter-specific techniques such as substitution/removal of URLs, of user mentions, of hashtags, and of emoticons, spelling correction, elongation normalization, abbreviation lookup, punctuation removal, detection of amplifiers and diminishers, negation scope detection, etc.
For this, one typically uses Twitter-specific NLP tools such as part-of-speech and named entity taggers, syntactic parsers, etc. \cite{gimpel2011,kong-EtAl:2014:EMNLP2014,ner}.

\textbf{Negation handling.} Special handling is also done for negation.
The most popular approach to negation handling is to transform any word that appeared in a negation context by adding a suffix \textit{\_NEG} to it, e.g., \textit{good} would become \textit{good\_NEG} \cite{Das2001,Pang:2002:TUS}.
A negated context is typically defined as a text span between a negation word, e.g., {\it no, not, shouldn't}, and a punctuation mark or the end of the message. Alternatively, one could flip the polarity of sentiment words, e.g., the positive word \textit{good} would become negative when negated. It has also been argued \cite{Zhu2014} that negation affects different words differently, and thus it was also proposed to build and use special sentiment polarity lexicons for words in negation contexts \cite{Kiritchenko2014}.

\textbf{Features.} Traditionally, systems for Sentiment Analysis on Twitter have relied on handcrafted features derived from word-level (e.g., \emph{great}, \emph{freshly roasted coffee}, \emph{becoming president}) and character-level $n$-grams (e.g., \emph{bec}, \emph{beco}, \emph{comin}, \emph{oming}), stems (e.g., \emph{becom}), lemmata (e.g., \emph{become}, \emph{roast}), punctuation (e.g., exclamation and question marks), part-of-speech tags (e.g.,~adjectives, adverbs, verbs, nouns), word clusters (e.g., \emph{probably}, \emph{probly}, and \emph{maybe} could be collapsed to the same word cluster), and Twitter-specific encodings such as emoticons (e.g., \emph{;)}, \emph{:D}), hashtags (\emph{\#Brexit}), user tags (e.g., \verb!@!\emph{allenai\_org}), abbreviations (e.g., \emph{RT}, \emph{BTW}, \emph{F2F}, \emph{OMG}), elongated words (e.g., \emph{soooo}, \emph{yaayyy}), use of capitalization (e.g., proportion of ALL CAPS words), URLs, etc.
Finally, the most important features are those based on the presence of words and phrases in sentiment polarity lexicons with positive/negative scores; examples of such features include number of positive terms, number of negative terms, ratio of the number of positive terms to the number of positive+negative terms, ratio of the number of negative terms to the number of positive+negative terms, sum of all positive scores, sum of all negative scores, sum of all scores, etc.

\textbf{Supervised learning.} Traditionally, the above features were fed into classifiers such as Maximum Entropy (MaxEnt) and Support Vector Machines (SVM) with various kernels. However, observation over the SemEval Twitter sentiment task in recent years shows growing interest in, and by now clear dominance of methods based on deep learning. In particular, the best-performing systems at SemEval-2015 and SemEval-2016 used deep convolutional networks \cite{SemEval:2016:task4:SwissCheese,Severyn:2015:TSA:2766462.2767830}. Conversely, kernel machines seem to be less frequently used than in the past, and the use of learning methods other than the ones mentioned above is at this point scarce. All these models are examples of supervised learning as they need labeled training data.

\textbf{Semi-supervised learning.} We should note two things about the use of deep neural networks. First they can often do quite well without the need for explicit feature modeling, as they can learn the relevant features in their hidden layers starting from the raw text. Second, they have too many parameters, and thus they require a lot of training data, orders of magnitude more than it is realistic to have manually annotated. A popular way to solve this latter problem is to use self training, a form of semi-supervised learning, where first a system is trained on the available training data only, then this system is applied to make predictions on a large unannotated set of tweets, and finally it is trained for a few more iterations on its own predictions. This works because parts of the network, e.g., with convolution or with LSTMs \cite{dossantos-gatti:2014:Coling,Severyn:2015:TSA:2766462.2767830,wang-EtAl:2015:ACL-IJCNLP2}, need to learn something like a language model, i.e., which word is likely to follow which one. Training these parts needs no labels. While these parts can be also pre-trained, it is easier, and often better, to use self training.

\textbf{Distantly-supervised learning.} Another way to make use of large unannotated datasets is to rely on \emph{distant supervision} \cite{marchettibowick-chambers:2012:EACL2012}. For example, one can annotate tweets for sentiment polarity based on whether they contain a positive or a negative emoticon. This results in noisy labels, which can be used to train a system \cite{Severyn:2015:TSA:2766462.2767830}, to induce sentiment-specific word embeddings \cite{tang-EtAl:2014:P14-1}, sentiment-polarity lexicons \cite{MohammadKZ2013}, etc.

\textbf{Unsupervised learning.} Fully unsupervised learning is not a popular method for addressing sentiment analysis tasks. Yet, some features used in sentiment analysis have been learned in an unsupervised way, e.g., Brown clusters to generalize over words \cite{Owoputi12part-of-speechtagging}. Similarly, word embeddings are typically trained from raw tweets that have no annotation for sentiment (even though there is also work on sentiment-specific word embeddings \cite{tang-EtAl:2014:P14-1}, which uses distant supervision).

\section{Sentiment Polarity Lexicons}

Despite the wide variety of knowledge sources explored so far in the literature, sentiment polarity lexicons remain the most commonly used resource for the task of sentiment analysis.

Until recently, such sentiment polarity lexicons were manually crafted and were thus of small to moderate size, e.g.,
LIWC \cite{pennebaker01} has 2,300 words,
the General Inquirer \cite{inquirer1966computer} contains 4,206 words,
Bing Liu's lexicon \cite{Hu04} includes 6,786 words,
and MPQA \cite{Wilson05} has about 8,000 words.

Early efforts toward building sentiment polarity lexicons automatically yielded lexicons of moderate sizes such as the SentiWordNet \cite{swn,esuli:lrec2006}.
However, recent results have shown that automatically extracted large-scale lexicons (e.g., up to a million words and phrases) offer important performance advantages, as confirmed at shared tasks on Sentiment Analysis on Twitter at SemEval 2013-2016 \cite{nakov-EtAl:2016:SemEval,Semeval2013,Rosenthal-EtAl:2015:SemEval,rosenthal-EtAl:2014:SemEval}.
Using such large-scale lexicons was crucial for the performance of the top-ranked systems. Similar observations were made in the related Aspect-Based Sentiment Analysis task at SemEval 2014 \cite{Semeval2014task4}.
In both tasks, the winning systems benefitted from building and using massive sentiment polarity lexicons
\cite{MohammadKZ2013,Zhu_SemEval2014}.

\noindent The two most popular large-scale lexicons were the Hashtag Sentiment Lexicon and the Sentiment140 lexicon, which were developed by the team of NRC Canada for their participation in the SemEval-2013 shared task on sentiment analysis on Twitter.
Similar automatically induced lexicons proved useful for other SemEval tasks, e.g., for SemEval-2016 Task 3 on Community Question Answering \cite{SemEval2016:task3:PMI-cool,nakov-EtAl:2016:SemEval}.

The importance of building sentiment polarity lexicons has resulted in a special subtask \cite{Rosenthal-EtAl:2015:SemEval} at SemEval-2015 (part of Task 4) and an entire task \cite{SemEval:2016:task7} at SemEval-2016 (namely, Task 7), on predicting the out-of-context sentiment intensity of words and phrases.
Yet, we should note though that the utility of using sentiment polarity lexicons for sentiment analysis probably needs to be revisited, as the best system at SemEval-2016 Task 4 could win without using any lexicons \cite{SemEval:2016:task4:SwissCheese}; it relied on semi-supervised learning using a deep neural network.

Various approaches have been proposed in the literature
for bootstrapping sentiment polarity lexicons starting from a small set of seeds:
positive and negative terms (words and phrases).
The dominant approach is that of Turney \cite{turney2002thumbs}, who uses pointwise mutual information and bootstrapping to build a large lexicon and to estimate the semantic orientation of each word in that lexicon.
He starts with a small set of seed positive (e.g., \emph{excellent}) and negative words (e.g., \emph{bad}), and then uses these words to induce sentiment polarity orientation for new words in a large unannotated set of texts (in his case, product reviews). The idea is that words that co-occur in the same text with positive seed words are likely to be positive, while those that tend to co-occur with negative words are likely to be negative. To quantify this intuition, Turney defines the notion of sentiment orientation (SO) for a term $w$ as follows:

\begin{center}
\textbf{$SO(w) = pmi(w,pos) - pmi(w,neg)$}
\end{center}

\noindent where PMI is the pointwise mutual information, $pos$ and $neg$ are placeholders standing for any of the seed positive and negative terms, respectively, and $w$ is a target word/phrase from the large unannotated set of texts (here tweets).

A positive/negative value for $SO(w)$ indicates positive/negative polarity for the word $w$, and its magnitude shows the corresponding sentiment strength. In turn, $pmi(w,pos)=\frac{P(w,pos)}{P(w)P(pos)}$, where $P(w,pos)$ is the probability to see $w$ with any of the seed positive words in the same tweet,
$P(w)$ is the probability to see $w$ in any tweet, and $P(pos)$ is the probability to see any of the seed positive words in a tweet;  $pmi(w,neg)$ is defined similarly.

The pointwise mutual information is a notion from information theory: given two random variables $A$ and $B$, the mutual information of $A$ and $B$ is the ``amount of information'' (in units such as bits) obtained about the random variable $A$, through the random variable $B$ \cite{Church:1990:WAN:89086.89095}.

Let $a$ and $b$ be two values from the sample space of $A$ and $B$, respectively. The \emph{pointwise} mutual information between $a$ and $b$ is defined as follows:
\begin{equation}
pmi(a;b) = \log{\frac{P(A = a, B = b)}{P(A=a)\cdot P(B=b)}} = \log{\frac{P(A=a|B=b)}{P(A=a)}}
\end{equation}

$pmi(a;b)$ takes values between $-\infty$, which happens when $P(A=a,B=b)$ = 0, and
$\min\left\{ -\log P(A=a), -\log P(B=b) \right\}$ if $P(A=a|B=b) = P(B=b|A=a) = 1$.

In his experiments, Turney \cite{turney2002thumbs} used five positive and five negative words as seeds.
His PMI-based approach further served as the basis for the creation of the two above-mentioned large-scale automatic lexicons for sentiment analysis in Twitter for English,
initially developed by NRC for their participation in SemEval-2013 \cite{MohammadKZ2013}.
The \emph{Hashtag Sentiment Lexicon} uses as seeds hashtags containing 32 positive and 36 negative words, e.g.,  \texttt{\#happy} and \texttt{\#sad}.
Similarly, the \emph{Sentiment140} lexicon uses smileys as seed indicators for positive and negative sentiment, e.g.,  \texttt{:)}, \texttt{:-)}, and \texttt{:))} as positive seeds, and \texttt{:(} and \texttt{:-(} as negative ones.

An alternative approach to lexicon induction has been proposed \cite{severyn2015automatic}, which, instead of using PMI, assigns positive/negative labels to the unlabeled tweets (based on the seeds), and then trains an SVM classifier on them, using word $n$-grams as features. These $n$-grams are then used as lexicon entries (words and phrases) with the learned classifier weights as polarity scores.
Finally, it has been shown that sizable further performance gains can be obtained by starting with mid-sized seeds, i.e., hundreds of words and phrases
\cite{jovanoski-pachovski-nakov:2016:COLING2016}.

\section{Key Applications}

Sentiment analysis on Twitter has applications in a number of areas, including
political science \cite{Borge-Holthoefer:2015dz,Kaya:2013ca,marchettibowick-chambers:2012:EACL2012},
economics \cite{Bollen:2011bf,oconnor10},
social science \cite{Dodds:2011uq},
and market research \cite{Burton:2011sh,Qureshi:2013fb}.
It is used to study company reputation online \cite{Qureshi:2013fb}, to measure customer satisfaction, to identify detractors and promoters, to forecast market growth \cite{Bollen:2011bf}, to predict the future income from newly-released movies, to forecast the outcome of upcoming elections \cite{marchettibowick-chambers:2012:EACL2012,oconnor10}, to study political polarization \cite{Borge-Holthoefer:2015dz,Tumasjan10}, etc.

\section{Future Directions}

We expect the quest for more interesting formulations of the general sentiment analysis task to continue. We see competitions such as those at SemEval as the engine of this innovation, as they not only perform head-to-head comparisons, but also create databases and tools that enable follow-up research for many years afterward.

In terms of methods, we believe that deep learning \cite{dossantos-gatti:2014:Coling,Severyn:2015:TSA:2766462.2767830,wang-EtAl:2015:ACL-IJCNLP2}, together with semi-supervised and distantly-supervised methods \cite{Davidov:2010:ESL:1944566.1944594,tang-EtAl:2014:P14-1}, will be the main focus of future research. We also expect more attention to be paid to linguistic structure and sentiment compositionality \cite{Socher:2012:SCT:2390948.2391084,socher-EtAl:2013:EMNLP}.
Moreover, we forecast more interest for languages other than English, and for cross-lingual methods \cite{Kaya:2013ca,mihalcea-banea-wiebe:2007:ACLMain,Wan:2009:CCS:1687878.1687913}, which will allow leveraging on the rich resources that are already available for English.
Last, but not least, the increase in opinion spam on Twitter will make it important to study astroturfing \cite{Ratkiewicz:2011:TMS:1963192.1963301} and troll detection \cite{mihaylov-georgiev-nakov:2015:CoNLL,mihaylov-EtAl:2015:RANLP2015,mihaylov-nakov:2016:P16-2}.

\section{Cross-References}

Microblog Sentiment Analysis	100590

\noindent Multi-classifier System for Sentiment Analysis and Opinion Mining	351

\noindent Sentiment Analysis in Social Media	120

\noindent Sentiment Analysis of Microblogging Data	110168

\noindent Sentiment Analysis of Reviews	110169

\noindent Sentiment Analysis, Basics of	110159

\noindent Sentiment Quantification of User-Generated Content	110170

\noindent Social Media Analysis for Monitoring Political Sentiment	110172

\noindent Twitter Microblog Sentiment Analysis	265

\noindent User Sentiment and Opinion Analysis	192

\bibliographystyle{spmpsci}

redpajama_set_name: RedPajamaArXiv


The Volkswagen Type 1, commonly known as the Volkswagen Beetle, Volkswagen Bug and Volkswagen Käfer, is a two-door, four passenger economy car manufactured by German automakers Volkswagen from 1938 to 2003.
Please note that this article is not about the Volkswagen New Beetle (1997 – 2010) or the Volkswagen Beetle (A5) from 2011.
A hot rod bug. Click the image to see a video.
The Volkswagen Type 1 was produced in huge quantities and sold in many different parts of the world, which meant that a lot of people are familiar with the Volkswagen and there will be a lot of professional and hobby mechanics that knows how this car works. Also, finding spare parts or special parts that will fit the Volkswagen Type 1 is not a problem.
The Volkswagen Type 1 was always intended to be an economy car. It was affordable right from the assembly line and it was even more affordable as a second hand car. Today, prices have began to go up a bit for well-maintained Volkswagen Type 1 cars, since the car is no longer in production, but it is still possible to find a quite affordable Volkswagen Type 1 to hot rod. This is especially true compared to the current price of classic Ford Mode Ts and Model As, which used to be the go-to choice for hot rod builders in the United States but are now rather scarce and pricey.
Compared to many other cars, the Volkswagen Type 1 is easy to work with and the flat four motor is a joy to tune.
The Volkswagen Type 1 is cheap to maintain and finding spare parts is not difficult.
Unlike most other cars, the Volkswagen Type 1 has an air-cooled horizontally opposed engine. Compared to more complex water-cooled engines, the Type 1 engine is easy to repair, and modification also becomes easier with this configuration.
Keeping the weight over the rear wheels maximizes grip off the starting line when drag racing.
When a Volkswagen Type 1 is modified for drag racing, it is usually stripped of "unnecessary" weight and this further improves grip.
A volkswagen beetle turned into a hot rod is sometimes called a volksrod, especially by those who wish to make a distinction between Ford-based hot rods and other "rods".
Many volksrods are built using basic mechanical tools, welding equipment and inexpensive parts, but it is of course possible to use as expensive parts and methods as you like and can afford.
One example of a popular volksrod is the one where a classic Ford front axle has been installed to move the wheels forward, which gives the car a low and stretched look.
Another popular modification is to move the original axle beam forward or reverse the trailing/torsion arms and adjust the steering linkages.
When it comes to pure aesthetics, popular paint jobs include flat black with pin-striping.
Black Current is a Volkswagen Beetle that has been rebuilt from the foundations up and fitted with an electrical engine and the associated parts required for this engine. It is chiefly used as a drag racer, and serves a scientific purpose by pushing the borders of what's achievable with an electric car.
At the time of writing, the Black Current speed record stands at 135 miles per hour (217 km/h). In 2011, a Black Current created by Olly Young reach 0-60 mph (0-96.6 km/h) in 1.6 seconds, thus proving to be a whole second faster to 60 than the Bugatti Veyron.
The functional objectives for the Volkswagen was formulated by Adolf Hitler, who wanted to mass-produce an affordable car for the new German road network. Ferdinand Porsche was contracted to design and construct the Volkswagen.
Production commenced in 1938, but during World War II production numbers were kept low since resources were diverted to the war effort.
In 1938 – 2003 a total of 21,529,464 Volkswagen Type 1 were built. 15,444,858 of these were built in Germany (including 330,521 cabriolets) and approximately 3,35 million in Brazil. The Volkswagen Type 1 is the most-manufactured car of a single platform.
In Germany, the car was affectionately referred to as Käfer, which means beetle. This idea was used in the marketing of the car in several other countries, e.g. Beetle in English speaking countries, Escarabajo in Spanish speaking countries and Coccinella (ladybug )in Frech speaking countries.


We all love Pinterest! It is a great place to get inspired. But, lets be honest...sometimes the DIY thing is not that easy. There is much more behind the "beautiful pictures" and "easy step-by-step tutorials." We decided to make a dream catcher for our Valentine's Day window. The great thing about it was we used everything we already had at the store! That always make things easier and affordable. DIY and creativity allows you to learn along the way and you can create your own, unique thing. So here is what we did on a Monday afternoon. Maybe it will inspire you to try it too.
1. We started off with our big hula hoop! We cut up a cute, old lacy shirt and glued it around the hoop.
2. Then the hard part. I will be honest this took us a couple times to figure out. We molded our metal wire into a heart. This is going to be tricky, but don't get frustrated. You will definitely need some help from a friend. We intertwined the two ends in the middle.
3. Find the perfect spot in the middle of your hoop. Secure a couple sections with the fishing line. We chose the middle and bottom of the heart. Then secured two sides.
5. There are going to be some areas (the top of the heart) that will need to glued in place. The more secure the better.
6. Grab your old shirts and get cutting. This will be the hanging party of your dreamcatcher. We made our outside short and went larger toward the middle. Get creative and make the bottom fun!
Back in March, Ash came up to me and said, "What if we got The Green for our five-year anniversary?" I gave her that excited/worried face for a few seconds, and then said, "Lets do it!" We both had this dream of what we wanted that night to look and feel like. Two weeks ago our dream became a reality.
Once we found out The Green was in, everything became real. We unveiled our "SECRET" plan to our friend Kawika from Native 92.5. His face and excitement reassured us this was going to be something Maui has never experienced.
Besides planning the actual event, we had to make sure our new summer line was here on time. Design, order, print, ship, count, fold, tag, hang…..the long, but fun process of retail. Once the clothes were in, Ash could breathe a little?
Picking a fashion show theme isn't easy. The ideas are endless, but the execution makes things a little complicated. It only seemed right to theme our show with the music. The Green's hit song, "Alive," became the inspiration for our fashion show. Sixteen models and 43 different looks… we had our work cut out for us. Ideas were flying everywhere, photos, inspiration boards, creative conversations, the show consumed our every thought.
We wake up and all of a sudden, it is Friday, July 5 - the moment where all you can do is believe everything you have done will work out. Stage and runway construction began early morning. Ken, John and Kanoa definitely got their workout in, and quite a tan from constructing our set. The flowers were driven in from Hana. David and crew set-up lights and sound. The band arrived, and during sound check, a heavy, grey cloud lingered in Iao. We needed a tarp, and fast. Thank you Mr. Beauchamp for coming to the rescue. The Carpenter's Union constructed our backdrop and in a blink of an eye. Kanoa and Keone stapled their little hearts out as fast as they could. After a little mishap here and there and a couple small tweaks to the program, we're finally ready for this.
After running around like a chicken without a head, I finally got to join the rest of the models for some hair and make-up. At 7:10 p.m., the party bus arrived, confining the secret band. After Kawika hyped up the anxious crowd, out stepped six talented guys that might be referred to as the Beatles of Hawaii. Once the first chord was played, everything fell into place.
Behind a black, tinted door, the models were anxiously awaiting for "Travlah" to begin playing. With ti-leaf masks disguising our identities, we all became "The Green Girl," who adorns the cover of their albums. Each year Ash and I have this moment right before I walk on the runway. It is a moment of excitement, respect and appreciation for each other. We know everything, from the late nights to the stress, were worth this one night of excitement.
We cannot express how thankful we are for EVERYONE who helped make this night possible. Collaboration and cooperation are truly what makes things like this happen. Dreams become a reality when people work together with good intentions. This night was truly about celebrating with the people and community who continue to support our business and our hope to inspire through design.
Big mahalo to The Green! We are so proud of you guys and appreciate you sharing this night with us. See you in another FIVE YEARS!
Simply put, Mondo is genius. His creativity surpasses the everyday artist, the average fashion designer. Plaid and polka dots? Tribal and gold? On the runway, Mondo makes the outlandish pattern contrast and brightly fabulous colors work to his advantage. No matter how quirky and mismatched his outfits are, you're always able to see consistency, flow to his shows. Whether it be each model in a dramatic hair piece, bright pink lipstick, or simply a red umbrella, he amazes his crowds with the pure creativity of his pieces and uniformity in his runway shows. For the Maui show at the Hyatt, he had each model in polka dot socks with heels, a red umbrella, and (genius!) red and blue 3D glasses. In typical Mondo fashion, these berserk items pulled the show together! Mondo's patterns, shiny material, lovely models, use of color and odd accessories...purely fabulous. It was hot...just like Mondo.
we even got to meet him..thanks to VIP Style for bringing the designers to Maui!
Ashley came into the store the other day to do some shopping and I couldn't help but notice her great outifit..completed by her fedora! The easiest way to look cute and spice up a basic look is a cute hat! Plus..you don't have to worry about doing your hair :) So next time you think your wardrobe pick for the day is blah, grab a hat!


In many countries, liability insurance is a compulsory form of insurance for those at risk of being sued by third parties for negligence.The most usual classes of mandatory policy cover the drivers of vehicles, those who offer professional services to the public, those who manufacture products that may be harmful, constructors and those who.
The named non-owned auto policy was specifically developed to address this. is actually insuring the vehicle you're driving) on their Business Auto Policy.
OnGuard Insurance offers great low rates and immediate coverage for non owners insurance. Get an Online Non Owners Insurance Quote or call us at (888).
The state Attorney General, in an opinion the village requested in November 2017, ruled that Police Chief Harry Brignac's wif.
He falls behind on paying his auto insurance, traffic ticket. The state tacks on additional fees for drivers to get their licenses reinstated after the original financial obligations are met. This.
Don't own a car? You still might need your own auto insurance coverage. Non-owner car insurance is a special policy designed for people who don't have a car.
New York law requires that you have auto liability insurance coverage. maintain the coverage, the DMV can suspend your vehicle registration and your driver license. All Owned & Non-Owned – Comprehensive Auto Liability Policy (AON).
A Vancouver resident, who was born in Ontario but has been living in British Columbia for the last 8 years claims they are one of the first people in this province to apply for the short-form non-bina.
What is Non-Owner SR-22 Insurance and Why Would I Need It? Non-Owners SR -22 coverage is essentially insurance for your driver's license and allows you to.
With the three-day initial supply restriction, Haslam's office says, "Tennessee will have one of the most strict and aggressive opioid policies in the nation. examination and liability insurance to.
Add non-Obamacare. alien driver license and programs specialist Carlos Leon told the potential DACA/DAPA applicant that "there is insurance available for undocumented individuals and it does not ha.
Currently, only a few jobs have federally mandated drug testing: pilots, truck drivers. workplace policy, testing policies.
The device requires the driver to blow into it. Kim Reynolds said recently she does not believe any Iowa cities currently.
The remainder will compensate Saviatto for lost wages and any other non-wage based injuries. will be covered by an insuran.
This coverage can be called nonowners insurance, non-owners insurance, non. if you should be listed as a driver on their policy. Pegram Insurance.
Oklahoma still requires all drivers to have insurance whether you own a car or not. Get multiple quotes for non-owner auto insurance in Oklahoma online.
The Louisiana Office of Motor Vehicles is reviewing its photograph policy after. and gender non-conforming people in ID laws. Recent efforts by transgender people in South Carolina and West Virgini.
Jun 5, 2014. Liability coverage protects the insured (i.e., follows the driver) when. as a standard auto policy anymore and coverage for non-owned autos.
Jul 18, 2016. It's a great way for a primary driver, one who does not own a vehicle, to remain insured. One of the papers you will need to fill out for this is the.
The blanket query into whether or not insurance follows the car or the driver in a particular. and listed on the insured's policy." There really is no such thing as a standard auto policy anymore a.
UK insurance firm Admiral had intended to launch an app this week offering discounted car insurance premiums to first time drivers based on an algorithmic. has fallen foul of Facebook's platform po.
Landair also has a safe and experienced corps of professional drivers. insurance premiums, fluctuations in claims expenses that result from our self-insured retention amounts, including in our exce.
That required, first and foremost, an alliance with George Norcross III, the insurance executive who oversees South. with.
In California, 55 percent of all union members are civil servants — police officers, sheriff's deputies, teachers, firefighte.
Non Owner Insurance – What You Should Know. Many believe that a non owner insurance policy will cover you for. you cannot add any drivers to this type of policy.
The device requires the driver to blow into it. Reynolds said recently she does not believe any Iowa cities currently have.
Cheapest SR22 Insurance, both Owner and Non. How to Get Your Driver's. " Working with your agency for my Virginia non-owner FR44 Insurance policy was.
You asked if (1) an automobile insurance company has the right to demand information from a policyholder on other drivers in the household, (2) there is policy language regarding this, (3) there are ramifications for a policyholder who withholds such information, and (4) the insurer will pay a claim if the driver was not a named insured.
The Ohio Senate has launched hearings on legislation that would block payments for pain and suffering that result from car accidents for drivers who don't carry the statutory minimum insurance. ins.
Non owner insurance may be required for North Carolina drivers to obtain for the the. Our agents can help you obtain non owners insurance coverage today.
Cheapest non owners Kansas SR22 insurance quotes. Get cost, quote and policy and SR22 filed the same day. Call 1-866-663-7561 for faster service.
Most Arizona personal auto policies will provide coverage for driving in Canada, but you should review your policy or contact your insurer or insurance agent or producer before driving into Canada to determine if your coverage applies in Canada.
Most states require bodily-injury liability insurance to cover medical. by your own passengers, other drivers, their passengers and even injured pedestrians. Other costs covered include lawyers' fe.
Find out exactly how insurance works when you're driving someone else's car, provisions, limitations, or exclusions expressly stated in any insurance policy.
Non-owner car insurance is a special kind of policy that gives you liability coverage when you're. If you're a high-risk driver, however, you'll pay more.
Non Owners provides liability insurance for a driver even when that person doesn't. Many people who don't own a vehicle may need this coverage in order to.
It is important to note, too, that in New York even among non-union members. on public policy in many states, including defending vital public services against budget cuts. For example, New York's.


A tenement is a multi-occupancy building of any sort. However, in the United States it has come to mean a run-down apartment building, a slum. In parts of England, especially Devon and Cornwall, it refers to an outshot, or additional projecting part at the back of a terraced house, normally with its own roof.


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Diocese of Crookston
St. Joseph's, Fertile
Confirmation Requirements
Ministries Schedule
16180 336th Street SE Mentor, MN 56736
Saturday: 7:00 p.m. (Memorial Day weekend through Labor Day weekend)
Friday: 9:00 a.m.
Sunday: 10:30 a.m.
Before Mass or any time by appointment.
Online Donations to St. Lawrence
Click here to make a Donation
Catholic Link
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What One Catholic Learned From Marching With Martin Luther King Jr.
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U.S. bishop with a Salvadoran heart weighs in on historic beatifications
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redpajama_set_name: RedPajamaCommonCrawl


text:
The WoodenBoat Forum > The Bilge > 'Passchendaele'
View Full Version : 'Passchendaele'
I just finished Paul Gross' movie about the Canadian involvement in this major battle of the First World War. It was sentimental and predictable, and like 'Gallipolli' and 'the Lighthorsemen' it had as one of the major characters an evil Brit who didn't mind in the least these young 'colonials' giving up their lives for the Empire. I also have copies of 'Joyeux Noel', 'My Boy Jack', 'a Very Long Engagement'. 'Behind the Lines' (I think this is called 'Resurrection' in the UK) and probably a couple of others that don't immediately come to mind. WWI is such an interesting thing to study and think about because in our day and age who in their right mind would volunteer to go fight in such numbers for 'God, king, and country'? I mean, what is the current size of the US military in active field troops? Something like .5%? From the statistics qouted something like 8% of all of Canada participated in WWI. That's a very high number in comparison to the population.
We in the US do not study WWI in the way that it seems some of our ex-colonial cousins do. I assume that has to do with the fact that we found our identity independent from Great Britain much earlier, and that for us WWI was just another step in establishing ourselves as a world power rather than taking a step towards further independence from the 'mother' country. Personally the period is fascinating. One of my favorite books is a little thing I found in a bragain bin called 'the Officer's Ward', about French officers who have suffered severe facial trauma during the war and how they go about putting together some sort of lives in spite of that.
Anyone here know of a 'modern' film that features US involvement in WWI? The only one I can think of is 'Legends of the Fall'. Anything else?
Taylor Tarvin
We in the US do not study WWI in the way that it seems some of our ex-colonial cousins do.
I believe the reason for this is twofold. The first is our very late entrance into the war. Although we had over a million men in Europe by the time the war ended, and many units performed well in the campaigns we were involved in , it was insignificant to what the other allied nations contributed (i.e. lost).
We also didn't persue colonial ambibtions in the now defunct Ottoman empire like many of our allies did. These colonial aquisitions reshaped political debate, and realities with our allies that it didn't with us, at least not for several decades.
Andrew Craig-Bennett
Hang on a minute there.
First, for all European nations the First World War was and remains the greatest catastrophe that they ever underwent. Of course we are fascinated by it. For the USA it was not a catatrophe; fewer Americans died and the USA started to play a major international role at Versailles.
Second, let us not forget that Saudi Arabia is at least as much a creation of the United States as it is of any European states.
I have just spent many hours viewing numerous documentaries on WWI. This one, partially available on Youtube, is supposedly not otherwise available.
http://www.youtube.com/watch?v=xhbe7xVmC0U&feature=related
This one, about the battle for Vimy Ridge is also a fair look at the Canadian Army.
http://www.youtube.com/watch?v=iHj88WeLohk&feature=related
PBS showed an 8 hour WWI documentary called "The Great War" which may still be available. Very well done.
One thing that sticks with me after a steady diet of this stuff is it was all very gastly and the survivors were often as damaged as the dead. To think that the Germans jumped back into the fire after only a twenty year hiatus is also amazing.
I have anumber of flics about the war, both versions of "All's Quiet.....", "The Big Parade", "Wings" but the one most memorable is "Paths of Glory" (1957) directed by Stanley Kubrick.
US troops didn't go into serious action till 1918 even though they had started arriving in 1917. The awareness of just what impact WW1 had on Europe is going to be far greater in Europe than in the US. There are still many towns in France and Belgium where flowers are placed on graves
http://www.youtube.com/watch?v=vWc9EvjxAk4
People of my generation in Europe knew, as children and as young adults, men who had been subalterns in WW1. The two dimensional representations of them in recent fiction are irritating.
A very good WW1 book is Somme Mud by Private E.P.F Lynch.
Here's a description.
Somme Mud tells of the devastating experiences of Edward Lynch, a young Australian private (18 when he enlisted) during the First World War when he served with the 45th battalion of the Australian Infantry Forces on the Western Front at the Somme, which saw the most bloody and costly fighting of the war. In just eight weeks, there were 23,000 Australian casualties.
The original edition of twenty chapters, was written in pencil in twenty school exercise books in 1921, probably to help exorcise the horrendous experiences Private Lynch had witnessed during his three years at war from mid-1916 until his repatriation home in mid-1919. Lynch had been wounded three times, once seriously and spent over six months in hospital in England.
Published here for the first time, and to the great excitement of historians at the War Memorial Somme Mud is a precious find, a discovered treasure that vividly captures the magnitude of war through the day-to-day experiences of an ordinary infantryman. From his first day setting sail for France as the band played 'Boys of the Dardanelles' and the crowd proudly waved their fresh-faced boys off, to the harsh reality of the trenches of France and its pale-faced weary men, Lynch captures the essence and contradictions of war.
Somme Mud is Australia's version of All Quiet on the Western Front. Told with dignity, candour and surprising wit, it is a testament to the power of the human spirit, a moving true story of humanity and friendship. It will cause a sensation when it is published.
ISBN / Catalogue Number: 9781741668940
It's fair to mention that America wasn't in the WWII for a while either.
They didn't leave it quite so late for the 2nd one and made up for it with interest:D
SMARTINSEN
The Guns of August and the prequel, The Proud Tower by Anne Tuchman are a good insight into the genesis of WWI. Like WWII may be considered the final act of WWI, the unfinished business of the continental wars of the middle 1800s set the stage for August 1914.
Sorry, I cannot recommend anything on film.
I quite agree with you except that it was Barbara Tuchman who wrote "The Guns of August." She is a methodical historian, with a wide range of interests not always the easiest to read. Was Ann was her hot younger sister? :)
I'd guess a definitive American movie about the Great War has not been made probably because the trauma was very largely European. I have always fancied "Paths of Glory" but it is not an American topic. Mick, have you watched any of the History Channel offerings? They are all British, and they are all quite well done. The series using original footage is quite moving. I think it is simply called "World War I." But it is not a "movie."
If we are recommending WWI books, I'd add August 1914 by Solzhenitsyn about the early actions on the Eastern Front.
I find it hard to wrap my head around the horrors of trench warfare, especially during the sharp, prolonged engagements that ground the armies into mud. The prolonged hopelessness of those places seems unendurable.
I think there's little doubt that the over-exploitation of the colonial troops by the Brits has had a lasting impact. Read Les Carlyon's "The Great War" to gain an understanding of that exploitation.
The first Battle of Passchendaele (Oct 12) didn't involve the Canadians, incidentally. There were 5 British, 2 Australian and 1 New Zealand brigades involved and wiki notes "more than 2,700 New Zealand casualties, of which 45 officers and 800 men were either dead or lying mortally wounded between the lines. In terms of lives lost in a day, this remains the blackest day in New Zealand's recorded history." Adolf Hitler was gassed here on Oct 13th, unfortunately, not well enough.
The second Battle of Passchendaele (Oct 26-Nov 10) had the Canadian Corps relieving the ANZACs (II Corps) on the left, with the I ANZAC Corps remaining on the right.
Re American involvement in WW1... their first offensive actions were in a much smaller battle, but what in many senses was the most important battle of WW1 (given subsequent events) - the Battle of Hamel on July 4 1918. Again, from wiki:
The Battle of Hamel (4 July 1918) was a successful attack launched by the Australian Corps (http://en.wikipedia.org/wiki/Australian_Corps) of the Australian Imperial Force (http://en.wikipedia.org/wiki/Australian_Imperial_Force_(1st)) and several American units against German (http://en.wikipedia.org/wiki/Germany) positions in and around the town of Hamel (http://en.wikipedia.org/wiki/Hamel,_Nord) in northern France (http://en.wikipedia.org/wiki/France) during World War I (http://en.wikipedia.org/wiki/World_War_I). The battle was planned and commanded by Lieutenant General John Monash (http://en.wikipedia.org/wiki/John_Monash) (later knighted (http://en.wikipedia.org/wiki/Knight)). Many of the tactics used were illustrative of the departure from the largely unsuccessful tactics of earlier years and the development of modern military tactics such as the use of combined arms (http://en.wikipedia.org/wiki/Combined_arms), which had previously been implemented in the Battle of Cambrai (1917) (http://en.wikipedia.org/wiki/Battle_of_Cambrai_(1917)). The battle was a success, with all objectives being achieved only 3 mins over the planned battle time of 90 minutes. In previous battles, using conventional tactics, the fighting could have lasted for weeks or months with much higher casualty rates. For example, a similar defensive position had resisted capture for two months in the Battle of the Somme (http://en.wikipedia.org/wiki/Battle_of_the_Somme). The battle was notable because it was the first time in the war that American troops participated in an offensive action and it was the first time that American troops served under non-American command. Four American companies participated with Australian troops under Australian command (although three of the companies were recalled before the battle).[2] (http://en.wikipedia.org/wiki/Battle_of_Hamel#cite_note-1) There were 1062 Australian casualties (including 800 dead), as well as 176 American casualties (almost 100 dead), while there were probably 2000 Germans killed and 1,600 captured, along with the loss of much of their equipment.
The tactics used in the Battle of Hamel became the basis of Blitzkreig, used to brutal effect by the Germans in WW2.
JayInOz
Several members of my family fought in the first world war. The stories told by those who survived of the carnage under British command were bloody horrendous. My grandfather said it was like reverse culling livestock- they took the absolute strongest and best and mowed 'em down. My Dad will be 85 in a few days, and he still gets emotional talking about it. JayInOz
Some good discussion here from differing points of view. I appreciate the input.
In Paul Gross' movie one of the main characters is someone who never appears on film: the father of two other characters who was born in Germany and immigrated to western Canada. He goes back to Germany and volunteers, is killed during Vimy Ridge, and leaves his daughter and son not only having to fend for themselves financially while dealing with a ton of emotional baggage, but being ostracized and persecuted by their community. I have always hear that one of the reasons for President Wilson's reluctance to join into the fight was that there were so many German-speaking Americans in this country that he did not know what would happen if the US did throw in with the Allies. Financially the US could not afford to break away from Britain and France, but the entire midwestern US was settled by people of German and Scandinavian descent (in large part), and that made things a little dodgy. I think that Wilson felt like there had to be concrete justification or we would sit it out, and perhaps the sinking of the 'Lusitania' was that justification?
My grandfather Nolan Lake was a solider in the Great War in France. He was with the US cavalry in Mexico and on the border there and served in France. I have photos of he and his trusty mule. One of things that I was always told about that war is that the French insisted that if we (Americans) were going to serve over there that we must do it under their command, which not only violated our Constitution, but tore at the pride of every American that volunteered to go fight. The feeling here was that whatever victory there would eventually be would be a French victory and not because the Allies stepped in to assist them. I have read many books about the Great War, but it never fails to surprise me, the things that went on.
One last thing, I have an old book around here somewhere called the Great War in Africa, which focuses mainly on the struggle in east Africa between the South Africans and the Germans and their askari allies commanded by Paul Von Lettow-Vorbeck (sic?). Absolutely fascinating stuff. The Germans had planned German East Afrika as a model colony and had all sorts of factories and labs there, manufacturing all sorts of 'ersatz' goods well before the War began. Von Lettow never lost a battle, though he did yield the field, and when the war was over in Europe he had to be ordered to cease fire on the South Africans and their native allies. A fascinating man.
Ah well. It's been nice talking with you guys. I am off to the airport for a flight to Germany. Be gone two weeks.
The histories that I have read put the French and British attitude to US forces a little differently; France and Britain wanted US troops in the front line but were concerned about the length of time that would be needed to bring the US Army, which for most practical purposes was a volunteer citizen army like Kitchener's Army, up to the level of training required for the Western Front. They therefore wanted US units to be integrated with their own forces.
Pershing insisted on the US Army fighting as a unit; he accelerated the training as much as possible and got US troops into the line in quantity by the summer of 1918.
It's a bit of a forgotten issue; we need to look back and remind ourselves of what a tiny, but professional, force, the US Army was before WW1.
Oh, I agree with you A C-B. And that's kind of along the lines of what I have read, but maybe my stuff is coming from a bit of a more political angle from an American point of view.
When the Spanish-American War broke out there was a good reason why the American army had to depend so heavily on non-professional volunteers. Most of our army was spread out in places like California and Texas along the borders with Mexico (with whom we had had a war only 50 years earlier) or out on the frontier. We had a small but effective navy and we had Marines, but we did not have much of an army.
Andrew, I didn't mean to give the impression that we weren't involved in the middle east until the later 20th century, we just didn't get involved as soon as Britain and France. If the cause of current problems were as simple as the third post would indicate the solution would be simple.
Mickey, We didn't get involved until late in the war because of our isolationist policies and public attitude. Wilson was reelected running as an ani-intervention president. If you were to ask most americans why we became involved in WWI you would probably get a slackjawed blank stare. Some would reply that it was the sinking of the Lusitania which is partially correct. The Lusitania was sunk in 1915, two years before our entrance in the war. There was such an international outcry after the sinking that Germany recinded thier unrestricted warfare policy. When Germany announced it would resume thier unrestricted U-boat policy in early 1917 Wilson used the rally cry of the Lusitania to nudge the american public towards intervention. When Germany realized that America might become involved they started negotiations with Mexico to declare war on the US if we declared war on Germany. That probably sealed the deal as far as Wilson was concerned
Yes... that issue of green troops coming into a different style of war is what was behind the US troops fighting under Monash at Hamel. Pershing ordered it not happen I believe, and some units were withdrawn. Monash wanted to ensure they went in with experienced troops. Some did. Pershing's policy resulted in a lot of unecessary deaths... not that that was an unusual thing in a war that killed 37 million people.
Flying Orca
Some novels that delve into the Great War with varying degrees of success include Mark Helprin's A Soldier of the Great War (one of my favourite novels, but not for its WWI content), Joseph Boyden's Three Day Road (highly recommended), and the vastly underappreciated Martin Booth's Islands of Silence, which I defy anyone to read without tears.
Kevin G
Jay in Oz:
I misunderstand your last post, about your g'pa. If he is just 85, he would have been born 1925, long past WW1. This certainly did not make WW2 any more pleasant. My brother-in law served in Korea, and when he come back, he never was the same, quiet and reserved after that. It doesn't seem that any of them are any fun. What is the purpose?
keving
http://www.youtube.com/watch?v=TGVmOS9yM6M&feature=related
stevebaby
Yes... that issue of green troops coming into a different style of war is what was behind the US troops fighting under Monash at Hamel. Pershing ordered it not happen I believe, and some units were withdrawn. Monash wanted to ensure they went in with experienced troops. Some did. Pershing's policy resulted in a lot of unecessary deaths... not that that was an unusual thing in a war that killed 37 million people.In the closing days of the war,when it became obvious that the war would soon be over,there were a number of attacks ordered by American officers. The cynical have claimed that this was so those officers could receive medals with which to enhance their post-war careers. Being later to join the conflict than their Allied counterparts in the British and French armies,the Americans felt deprived of the opportunity to demonstrate their bravery,and sent their own troops into battle to make up the difference.
Hard to believe,I know.
A very fine novel about how WWI affected the social fabric of Europe is "Birdsong" by Sebastian Faulks. While it contains some wrenching portrayals of the battlefield, its focus is also on those who survived and their relationships. A superbly written book! Love, horror, fear, redemption, all wrapped in very readable prose.
On Steve's point, a recent BBC programme on the last morning of the War - the hours leading up to the agreed cease fire at 11.00 am on the 11th November 1918 - illustrated that point very clearly - the only people who were pressing forward attacks were American, and they had the highest casualties. One unit was ordered into attack by its commanding officer because he had heard that it was possible to get a hot bath in the town they were facing.
On a more cheerful note, I liked the account of the German machine gunner who, at 11.00 precisely, stopped firing, stood up, waved and bowed to his opponents!
Yes, that one gets a vote from me.
The movie Fly Boys was actually pretty good. At least some great flying sequences.
http://images.moviepostershop.com//flyboys-movie-poster-2006-1010378580.jpg
Watch the movie here on hulu:
http://www.imdb.com/rg/VIDEO_PLAY/LINK//video/hulu/vi1041303321/
"On a more cheerful note, I liked the account of the German machine gunner who, at 11.00 precisely, stopped firing, stood up, waved and bowed to his opponents!"
On a similar note, I remember a story of a Christmas Armistice when troops on both sides came out of their trenches, shared their meager rations, sang songs, etc. "We are comrades, even though we are trying to kill each other." As if trying to kill each other had nothing to do with a recognition of the other's basic humanity.
Humans are a strange bunch.
wardd
that really ticked off the higher ups on both sides
His Father is 85.
Jay:
Mea culpa... I guess that's what I get for not reading carefully enough
Captain Intrepid
I always choke up when I think about George Lawrence Price of the 28th Battalion, Canadian Expeditionary Force. Killed at 10:58, 2 minutes before the armistice came into effect. He exemplifies for me the sheer waste and uselessness of the conflict.
Both sides used up as much ammunition as possible right up to the minute to avoid having to carry it all back to the supply dumps.
My Grandfather served both at the Somme and Galipoli, he ended his service in the 1960s with the Foriegn Service in the Sudan and Libya. He died blind at the age of 98, having seen queen Victorias funeral as a boy, to supersonic flight as an elderly man. As a boy he told me much of his life, and he had a lot to tell. He never talked of the Western Front. When I cleaned out may parent's house after their passing, I found a pack of letters he sent to my Grandmother, I also found his diarys. Comparing what he wrote home, with the equivilent dates in his diary, there was no comparison. One talked of food, birdsong, Spring time and Summer. The other speaks of anguish, foul stench, freinds disapearing yards from him only to find boots. I shook and cried, I still have not reopened the diarys in 10 years. His handwriting started off in typical Edwardian cursive caligraphy, and at the end was a blotted, erratic, and heavy handed, letters different sizes, scrawl. When I think of him I see a dignified tall man with a chest of medals, standing to attention at 90 in front of his wheel chair. Armistice day, Poppies, tears on his cheek.
God bless them all, on all sides.


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text:
Warner-Lambert Withdraws Diabetes Pill Rezulin
March 22, 2000 10:53 PM Subscribe
Imagine how it feels hearing this news about six hours after my doctor told me "After looking at your latest liver test, you'd better stop taking the Rezulin, and come in for more tests."
For the record, I was aware of the potential risks before I & my doc started it, and have been doing regular liver tests all along; before my last test, I had noted to doc that I was getting more symptoms of out-of-control diabetes, even though frequent monitoring showed the blood sugar numbers were always IN control, and I was ready to ask for a change to one of the newer drugs... when my latest refill runs out. I'd always assumed that by the time the FDA acts on it it'll be too late, and I am very interested in the official FDA non-position on the safety of the newer drugs...
posted by wendell (2 comments total)
Thank god I never was prescribed this pill. I have diabetes and I take Avandia. I have tight control over mine.
The FDA really needs to think up a better system when it comes to testing and approving new drugs. Stories like this are being coming more and more common.
It isn't people's health that drives drug companies to create new products, it is money, and the risk of side-affects takes a back seat to money. Look at phen-phen.
posted by da5id at 5:26 AM on March 23, 2000
i have had terrific success with prandin and glucophage in tandem with diet and exercise...no side effects at all except the occasional low blood sugar reaction...
posted by centrs at 10:40 AM on March 23, 2000
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text:
Maria Cucurull Canyelles (Valls, 1983) és escriptora i professora andorrana. Llicenciada en Filologia Catalana, ha estat lingüista a RTVA i actualment és professora de llengua i literatura catalanes a l'Escola Andorrana i la Universitat d'Andorra.

Ha estat articulista en diversos mitjans com El Periòdic, Ara.ad, Canal Català, Ràdio Nacional d'Andorra, Culturàlia i Núvol. Des del 2020 elabora la crònica semestral sobre la legislació lingüística a Andorra que publica la Revista Llengua i Dret. Actualment també és col·laboradora a Becaris de Ràdio Nacional d'Andorra.

Obres
 Jo en dic llom però l'àvia en deia llomillo. Llamps i Trons, 2022. (ISBN:9789992033074)
 100 coses que has de saber sobre Andorra. Anem, 2022 (ISBN: 9788418865077)
 Som llengua. Biblioteca de Núvol, 2022 (ISBN: 9788417455439)

Referències

«Com en diem, d'un 'gatillazo'?». Andrés Luengo, Diari Bon dia.  22/04/2022

«"És el llibre ideal per posar a prova la teva andorranitat"». Diari d'Andorra. 29/03/2022

«Maria Cucurull: "Busca la casella que multiplica; també a la vida"». Periòdic d'Andorra 25/10/2012

Filòlegs de l'àmbit catalanoparlant
Escriptors andorrans en català
Vallencs

redpajama_set_name: RedPajamaWikipedia


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text:
Q: .gitignore files not ignored when pulling remote changes I have some files that I don't want to share between my repositories. I added them to .gitignore at the root of my project :
# Specifies intentionally untracked files to ignore when using Git
# http://git-scm.com/docs/gitignore
# ...
.idea/

And yet I keep getting this error when pulling remote changes using git pull :
error: Your local changes to the following files would be overwritten by merge :
      .idea/workspace.xml
Please, commit your changes or stash them before you can merge.
Aborting

I have seen some questions about this error message, but they were about overwriting the files. I want to ignore the changes (I don't want git to track them at all and I don't want git to change my local files).
What am I doing wrong ? Maybe the problem is I created the .gitignore file after I had already committed the files I wanted to ignore, and I need to remove them from the repo somehow...?

A: If you don't want to track them, you need to remove them from the history first, and push that removal
git rm --cached -- afile
git add -u .
git commit
git push

As soon as the file is removed locally (the --cached keeps it on your disk), your .gitignore will work.
But any git pull would get back that file if it was versioned on the remote repo side. Hence the need to push (publish) that removal, provided that you are sure every other user should not see that file anymore as well.

A: .gitignore - Specifies intentionally untracked files to ignore
In this case your .idea directory is already tracked in git. So you need to remove it from repo first.
After that if you add .idea folder into your .gitignore file, it won't show up among untracked files.

A: Don't forgot to commit and push your gitignore file.

redpajama_set_name: RedPajamaStackExchange


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text:
#pragma once

#include <scraps/config.h>

#include <scraps/net/Endpoint.h>

namespace scraps::net {

class UDPReceiver {
public:
    virtual ~UDPReceiver() {}

    virtual void receiveUDP(const Endpoint& sender, const void* data, size_t length) = 0;
};

} // namespace scraps::net

redpajama_set_name: RedPajamaGithub


Law Web: Whether prosecution case can be doubted on ground of non recovery of weapon of offence?
Whether prosecution case can be doubted on ground of non recovery of weapon of offence?
The next line of contention taken by the learned counsel for the respondents is that the recovery evidence was false and fabricated. We feel no need to address this issue since it had already been validly discarded by the Trial court while convicting the respondents. In any case, it is an established proposition of law that mere non-recovery of weapon does not falsify the prosecution case where there is ample unimpeachable ocular evidence. [See Lakahan Sao Vs. State of Bihar and Anr., (2000) 9 SCC 82; State of Rajasthan Vs. Arjun Singh & Ors., (2011) 9 SCC 115 and Manjit Singh and Anr. Vs. State of Punjab, (2013) 12 SCC 746].


text:
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To NautaDutilh.com
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Membership is available for physicians of Indian descent who are actively practicing medicine, participating in research and teaching in the medical community in Hampton Roads and northeastern North Carolina. Medical students, residents in training and retired physicians are welcome as honorary members.
How often does AAPI-HR meet?
Meetings are held four times a year which includes an educational speaker and dinner. For information on the the next meeting, click here.
Annual dues are $100 per physician. Pay dues here.
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Mail the application with a check made out to AAPI-HR to P.O. Box 6428, Portsmouth, VA 23703-6428 or bring to the next scheduled meeting. For information on the next meeting, click here. For more information, email aapihamptonroads@gmail.com.


Originally published in 1959, Yes Mrs. Williams has long been unavailable. In recalling one of the "determined women" in his life. William Carlos Williams, the quintessentially American poet of this century, does not write about his mother so much as recreate her. An experimentalist in prose as well as poetry, Williams records the "talk" of Raquel Hélène Rose Hoheb Williams, capturing the contradictions of this Spanish-speaking, Puerto Rican-born, Parisian-trained artist turned New Jersey wile and mother, her strength and cantankerousness, her vitality and sense of failed purpose. For this first New Directions paperbook edition, Dr. William Eric Williams, son and grandson, has written an illuminating foreword that includes newly discovered Williams family letters.


text:
TJ Doyle – Singer. Musician. Songwriter. Humanist
TJ Doyle, is an American artist with a world view, a love of Earth and a feel for what it takes to care for the planet we are on. He also takes a song, wraps it in melodic yet compelling music and delivers it with a sense of fun, responsibility and subtle but strong Americana sensibilities. The result is amazing success on the world's stage including entering 2018 in the #1 position on the Radio Indie Top 40 Charts with 2 new releases from his 2nd album "Everything" and "Living In Our Dream".
Sitting at or near the top of the charts is not new for TJ Doyle. During the year 2017 he was in the Top 5 of the Indie Top 40 from late summer with his song "Anonymous" followed by "Unconditional", and now "Living In Our Dream".
TJ was nominated for the prestigious Round Glass Award in the Rock Song category with "Anonymous". The Award is designed to reward musicians who create socially responsible music for Wellness, Environmental Consciousness and Peace. TJ says, "Even to be nominated makes me feel honored, humbled and "Anonymous" is a tribute to anyone who has felt marginalized", says Doyle. As TJ's lyrics say, "Don't you know that you are golden, Don't believe you're anonymous".
In addition, various songs by TJ have been played on well over 600 radio stations over the past year and continue to be heard in regular rotation.
TJ has been lauded by critics for having his own musical and vocal styles, although he admits to his strong vocals being reminiscent of Neil Young . "That's the way I sing. Can't help it".
Raised in 5 Eastern cities by the age of 10, TJ now lives in a community on the edge of the Angeles National Forest above LA where he writes songs that transmit an authenticity harking back to the 60s. Does "harking back" mean he lives in the past? Not at all. "That era of music was when artists were writing about the challenges of a generation instead of the more personal and relationship-oriented pop and rock that currently dominates the airwaves." He goes after that vision delivering his passion for the environment. "Nature is free from the human condition that afflicts humanity, and is an inspiration that can help us all. By observing nature we can learn more about our own true natures."
Musically TJ, who has a touring history covering the northern American Midwest and Southern California, surrounds himself with some of the top musician's musicians to be found today on his recordings like Rock Deadrick, Drums & Percussion (Tracy Chapman) David Sutton, Bass (Tracy Chapman) Matt Laug, Drums (Slash's Snakepit), Lance Morrison, Bass, (Don Henley) Tim Pierce, Guitar (Crowded House, Dave Matthews) or in his live performances like Phil Parlapiano, Keyboards, (Lowen & Navarro), and Hank Van Sickle, Bass, (John Mayall). Legendary venues such as The Whiskey and The Roxy have hosted his band, "but it's time to expand on that. The plan is to get out into the rest of the world," fostered recently by extensive airplay in Europe, from his most recent album "On The Horizon". The TJ brand of music is also picking up steam in Australia, New Zealand and, of course, across North America.
At a time when divisions seem to be so loud in people's minds, TJ Doyle feels music can bring calm, so he presents ideas that help dissuade the internal discourse that is prevalent in our society today. In "Anonymous" particularly, the singer/songwriter says, " It is my hope the listener can take away the thought they have something very special in them, and start to fan the flames of this 'self', so they can 'spread their wings and FLY'!"
Doyle, describes his music in general as a vehicle to "help people make sense of themselves and the world. That is my prime directive with my music and lyrics."
"Anonymous", produced by David Z Rivkin (Prince, Fine Young Cannibals) is part of a planned EP, "or maybe an album. "It doesn't matter anymore. People can now enjoy music from an artist one song at a time. The album will arrive when it's the 'right time
Incendiary American: "As TJ Doyle Music releases albums and singles, you can hear that he's more than just a songwriter. He's a visionary whose work calls for the collaborative effort of musicians who are determined to see it through. Once the textures and dynamics in the music are appropriately executed, Doyle's unique voice makes that vision complete."
Stream TJ Doyle on SoundCloud today!
https://soundcloud.com/tjdoylemusic
Heart's Eyes
New Single Now Available
Download on iTunes or Follow on Spotify
From TJ Doyle's Up and Coming 3rd Album
Full Album Available
Now on Spotify
© 2019 TJ Doyle Music