What is the best performing model?
An ensemble of N-Channels ConvNet and XGboost regressor model
This paper describes our system that has been used in Task1 Affect in Tweets. We combine two different approaches. The first one called N-Stream ConvNets, which is a deep learning approach where the second one is XGboost regresseor based on a set of embedding and lexicons based features. Our system was evaluated on the testing sets of the tasks outperforming all other approaches for the Arabic version of valence intensity regression task and valence ordinal classification task.
Sentiment analysis in Twitter is the problem of identifying people’s opinions expressed in tweets. It normally involves the classification of tweets into categories such as “positive”, “negative” and in some cases, “neutral”. The main challenges in designing a sentiment analysis system for Twitter are the following: Most of the existing systems are inspired in the work presented in BIBREF0 . Machine Learning techniques have been used to build a classifier from a set of tweets with a manually annotated sentiment polarity. The success of the Machine Learning models is based on two main facts: a large amount of labeled data and the intelligent design of a set of features that can distinguish between the samples. With this approach, most studies have focused on designing a set of efficient features to obtain a good classification performance BIBREF1 , BIBREF2 , BIBREF3 . For instance, the authors in BIBREF4 used diverse sentiment lexicons and a variety of hand-crafted features. This paper proposes the representation of tweets using a novel set of features, which include the information provided by seven lexicons and a bag of negated words (BonW). The concatenation of these features with a set of basic features improves the classification performance. The polarity of tweets is determined by a classifier based on a Support Vector Machine. The system has been evaluated on the Arabic and English language test sets of the Twitter Sentiment Analysis Track in SemEval 2017, subtask A (Message Polarity Classification). Our system (SiTAKA) has been ranked 8th over 36 teams in the English language test set and 2nd out of 8 teams in the Arabic language test set. The rest of the paper is structured as follows. Section SECREF2 presents the tools and the resources that have been used. In Section SECREF3 we describe the system. The experiments and results are presented and discussed in Section SECREF4 . Finally, in the last section the conclusions as well as further work are presented.