基于机器学习的Yelp评论情感分析

H. S., Ramathmika Ramathmika
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引用次数: 15

摘要

情感分析是分析作者所写的一篇文章,以识别和分类文本中隐藏的观点,并确定作者对该主题的观点是积极的,消极的还是中立的过程。Yelp是一个评论论坛,提供对当地企业的评论。世界各地的用户都可以在这个社交网站上发布评论和评价任何企业。本文分析了yelp上商家的文本评论,为评论分配了积极或消极情绪的概率。情感分析考虑的数据是对餐馆的食物,服务,价格和氛围的评论。python的nltk库中的机器学习算法可以证明在任何此类自然语言处理研究中非常有用,并且该库已广泛用于这项工作。对所使用的每种算法进行了分析,并根据其效率(置信度)进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment Analysis of Yelp Reviews by Machine Learning
Sentiment analysis is a process of analyzing a piece of text written by a writer to identify and classify the opinions buried in that text and to determine whether the views of the writer about the topic is positive, negative, or neutral. Yelp is a review forum which provides reviews on local businesses. Users from anywhere in the world can post reviews and rate any business in this social networking site. In this paper, the textual yelp reviews of businesses are analyzed to assign a probability for the review as having positive or negative sentiment. The data considered for the sentiment analysis are the reviews on restaurants about food, service, price and ambience. Machine learning algorithms in the nltk library of python can prove to be very useful in any such research on Natural Language Processing and the library has been used extensively in this work. Each algorithm used has been analyzed and has been compared on the basis of their efficiency (confidence).
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