Sentiment Analysis Using Naive Bayes Approach with Weighted Reviews - A Case Study

Brandon Joyce, Jing Deng
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引用次数: 2

Abstract

Online reviews are critical in many aspects, for business as well as customers. Yet the accuracy and trustworthiness of these reviews are usually unsubstantiated and little research has been performed to investigate them. In this work, we use a set of Yelp reviews on various topics (food, hotel, etc.) as an example to perform sentiment analysis and investigate the correlation between review comment sentiment and its numeric rating. We use feature selection techniques to statistically remove redundant words from reviews, thus improving run time and accuracy. Our method gives higher weight to those terms/words appearing in reviews with more useful votes. These techniques combined with Naive Bayes approach achieves an overall accuracy of 75%. More interestingly, our method is shown to perform well in 1-star and 5-star reviews, with a 92% accuracy for the latter. With such a strong accuracy, we argue that the proposed sentiment analysis technique can be used to shed light on all online comments, especially those without numerical ratings.
基于加权评论的朴素贝叶斯方法的情感分析-一个案例研究
在线评论在很多方面都很重要,对企业和客户都是如此。然而,这些评论的准确性和可信度通常是未经证实的,而且很少有研究对它们进行调查。在这项工作中,我们使用一组关于各种主题(食物,酒店等)的Yelp评论作为示例来执行情感分析,并调查评论评论情绪与其数字评级之间的相关性。我们使用特征选择技术从评论中统计地删除冗余词,从而提高运行时间和准确性。我们的方法给予那些在评论中出现的有更多有用选票的术语/单词更高的权重。这些技术与朴素贝叶斯方法相结合,总体准确率达到75%。更有趣的是,我们的方法在1星和5星评论中表现良好,后者的准确率为92%。由于具有如此高的准确性,我们认为所提出的情感分析技术可以用于揭示所有在线评论,特别是那些没有数字评级的评论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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