Emotion and Sentiment Lexicon Impact on Sentiment Analysis Applied to Book Reviews

TL;DR

This paper investigates how emotion and sentiment lexicons influence the accuracy of sentiment analysis in Amazon book reviews, using a lexicon-based approach combined with supervised learning.

Contribution

It introduces a lexicon-based model incorporating emotional and sentiment words to improve sentiment classification of book reviews.

Findings

Emotion lexicons impact sentiment polarity detection.

Random Forest classifier achieves high accuracy with lexicon features.

Emotional words enhance sentiment analysis performance.

Abstract

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{\'e}s : Analyse de sentiments, Analyse d'{\'e}motions (texte), Classification de polarit{\'e} de sentiments