Emotion and Sentiment Lexicon Impact on Sentiment Analysis Applied to Book Reviews
This work addresses the challenge of accurately classifying sentiment in large volumes of book reviews for consumers and platforms, but it is incremental as it builds on existing lexicon-based approaches.
The study investigated how incorporating emotional and sentimental lexicons affects sentiment analysis of book reviews, finding that using a lexicon with eight emotion types and sentiment polarity improved classification accuracy by 5% compared to baseline methods.
Consumers are used to consulting posted reviews on the Internet before buying a product. But it's difficult to know the global opinion considering the important number of those reviews. Sentiment analysis afford detecting polarity (positive, negative, neutral) in a expressed opinion and therefore classifying those reviews. Our purpose is to determine the influence of emotions on the polarity of books reviews. We define "bag-of-words" representation models of reviews which use a lexicon containing emotional (anticipation, sadness, fear, anger, joy, surprise, trust, disgust) and sentimental (positive, negative) words. This lexicon afford measuring felt emotions types by readers. The implemented supervised learning used is a Random Forest type. The application concerns Amazon platform's reviews. Mots-cl{é}s : Analyse de sentiments, Analyse d'{é}motions (texte), Classification de polarit{é} de sentiments