论文标题
情绪和情感词典对账面评论的情感分析的影响
Emotion and Sentiment Lexicon Impact on Sentiment Analysis Applied to Book Reviews
论文作者
论文摘要
在购买产品之前，消费者习惯于咨询互联网上发布的评论。但是考虑到这些评论的重要数量，很难知道全球意见。情感分析在表达的意见中可检测到极性（正，负，中性），因此可以对这些评论进行分类。我们的目的是确定情绪对书籍评论极性的影响。我们定义了评论的“词袋”代表模型，这些模型使用词典，其中包含情感（预期，悲伤，恐惧，愤怒，喜悦，惊喜，信任，厌恶）和感性（正面，负面）单词。该词典提供了读者测量情绪类型的测量。所使用的有监督的学习是随机的森林类型。该应用程序涉及亚马逊平台的评论。 mots-cl {é} s：分析情感，分析d'{é}动议（texte），分类de polarit {é} de感性
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
