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Sentiment analysis of movie reviews: A study on feature selection classification algorithms

机译：电影评论的情感分析：特征选择与分类算法研究

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摘要

Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured or unstructured textual data. In this paper, we try to focus our task of sentiment analysis on IMDB movie review database. We examine the sentiment expression to classify the polarity of the movie review on a scale of 0(highly disliked) to 4(highly liked) and perform feature extraction and ranking and use these features to train our multi-label classifier to classify the movie review into its correct label. Due to lack of strong grammatical structures in movie reviews which follow the informal jargon, an approach based on structured N-grams has been followed. In addition, a comparative study on different classification approaches has been performed to determine the most suitable classifier to suit our problem domain. We conclude that our proposed approach to sentiment classification supplements the existing rating movie rating systems used across the web and will serve as base to future researches in this domain. "Our approach using classification techniques has the best accuracy of 88.95%".
机译：情感分析是一种宣布挖掘的子域，其中分析专注于从结构化，半结构化或非结构化文本数据中提取人民的情绪和意见。在本文中，我们尝试将我们对IMDB电影审核数据库的情感分析的任务集中。我们检查情感表达式，以将电影审查的极性分类为0（高度不喜欢）到4（非常喜欢的）和执行功能提取和排序，并使用这些功能培训我们的多标签分类器以对电影审查进行分类进入其正确的标签。由于缺乏非正式行话的电影评论中缺乏强大的语法结构，遵循了一种基于结构性N-GRAM的方法。此外，已经进行了对不同分类方法的比较研究，以确定最合适的分类器以适应我们的问题域。我们得出结论，我们提出的情绪分类方法补充了网络上使用的现有评级电影评级系统，并将成为该领域的未来研究基础。 “我们使用分类技术的方法具有88.95％的最佳准确性”。

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