  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
