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Further Experiments in Sentiment Analysis of French Movie Reviews

机译：法国电影评论情感分析的进一步实验

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摘要

In sentiment analysis of reviews we focus on classifying the polarity (positive, negative) of conveyed opinions from the perspective of textual evidence. Most of the work in the field has been intensively applied on the English language and only few experiments have explored other languages. In this paper, we present a supervised classification of French movie reviews where sentiment analysis is based on some shallow linguistic features such as POS tagging, chunking and simple negation forms. In order to improve classification, we extracted word semantic orientation from the lexical resource SentiWordNet. Since SentiWordNet is an English resource, we apply a word-translation from French to English before polarity extraction. Our approach is evaluated on French movie reviews, obtained results showed that shallow linguistic features has significantly improved the classification performance with respect to the bag of words baseline.
机译：在评论的情感分析中，我们专注于从文本证据的角度对传达的观点的极性（正面，负面）进行分类。该领域的大部分工作已广泛应用于英语，只有很少的实验探索了其他语言。在本文中，我们介绍了法国电影评论的监督分类，其中情感分析基于某些浅层语言功能，例如POS标签，分块和简单否定形式。为了改善分类，我们从词汇资源SentiWordNet中提取了词的语义取向。由于SentiWordNet是英语资源，因此我们在极性提取之前将词从法语翻译为英语。我们的方法在法国电影评论中得到了评估，获得的结果表明，相对于单词袋基线，浅层语言功能已显着改善了分类性能。

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