  In today's world, everyone is expressive in some way, and the focus of this
project is on people's opinions about rising electricity prices in United
Kingdom and India using data from Twitter, a micro-blogging platform on which
people post messages, known as tweets. Because many people's incomes are not
good and they have to pay so many taxes and bills, maintaining a home has
become a disputed issue these days. Despite the fact that Government offered
subsidy schemes to compensate people electricity bills but it is not welcomed
by people. In this project, the aim is to perform sentiment analysis on
people's expressions and opinions expressed on Twitter. In order to grasp the
electricity prices opinion, it is necessary to carry out sentiment analysis for
the government and consumers in energy market. Furthermore, text present on
these medias are unstructured in nature, so to process them we firstly need to
pre-process the data. There are so many feature extraction techniques such as
Bag of Words, TF-IDF (Term Frequency-Inverse Document Frequency), word
embedding, NLP based features like word count. In this project, we analysed the
impact of feature TF-IDF word level on electricity bills dataset of sentiment
analysis. We found that by using TF-IDF word level performance of sentiment
analysis is 3-4 higher than using N-gram features. Analysis is done using four
classification algorithms including Naive Bayes, Decision Tree, Random Forest,
and Logistic Regression and considering F-Score, Accuracy, Precision, and
Recall performance parameters.

Computation and Language