A Review on Lexicon-Based and Machine Learning Political Sentiment Analysis Using Tweets

Abstract

Sentiment analysis over social media platforms has been an active case of study for more than a decade. This occurs due to the constant rising of Internet users over these platforms, as well as to the increasing interest of companies for monitoring the opinion of customers over commercial products. Most of these platforms provide free, online services such as the creation of interactive web communities, multimedia content uploading, etc. This new way of communication has affected human societies as it shaped the way by which an opinion can be expressed, sparking the era of digital revolution. One of the most profound examples of social networking platforms for opinion mining is Twitter as it is a great source for extracting news and a platform which politicians tend to use frequently. In addition to that, the character limitation per posted tweet (maximum of 280 characters) makes it easier for automated tools to extract its underlying sentiment. In this review paper, we present a variety of lexicon-based tools as well as machine learning algorithms used for sentiment extraction. Furthermore, we present additional implementations used for political sentiment analysis over Twitter as well as additional open topics. We hope the review will help readers to understand this scientifically rich area, identify best options for their work and work on open topics.