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Sentiment Classification Of Online Reviews To Travel Destinations By Supervised Machine Learning Approaches

机译：监督机器学习方法对旅行目的地在线评论的情感分类

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摘要

The rapid growth in Internet applications in tourism has lead to an enormous amount of personal reviews for travel-related information on the Web. These reviews can appear in different forms like BBS, blogs, Wiki or forum websites. More importantly, the information in these reviews is valuable to both travelers and practitioners for various understanding and planning processes. An intrinsic problem of the overwhelming information on the Internet, however, is information overloading as users are simply unable to read all the available information. Query functions in search engines like Yahoo and Google can help users find some of the reviews that they needed about specific destinations. The returned pages from these search engines are still beyond the visual capacity of humans. In this research, sentiment classification techniques were incorporated into the domain of mining reviews from travel blogs. Specifically, we compared three supervised machine learning algorithms of Naieve Bayes, SVM and the character based N-gram model for sentiment classification of the reviews on travel blogs for seven popular travel destinations in the US and Europe. Empirical findings indicated that the SVM and N-gram approaches outperformed the Naieve Bayes approach, and that when training datasets had a large number of reviews, all three approaches reached accuracies of at least 80%.
机译：互联网在旅游业中的快速增长已导致对Web上与旅游相关的信息进行大量的个人评论。这些评论可以以不同的形式出现，例如BBS，博客，Wiki或论坛网站。更重要的是，这些评论中的信息对于旅行者和从业者来说，对于各种理解和计划过程都是有价值的。但是，Internet上大量信息的一个固有问题是信息过载，因为用户根本无法读取所有可用信息。诸如Yahoo和Google之类的搜索引擎中的查询功能可以帮助用户找到有关特定目的地的一些评论。这些搜索引擎返回的页面仍然超出了人类的视觉能力。在这项研究中，情感分类技术被纳入旅游博客的采矿评论领域。具体来说，我们比较了Naieve Bayes，SVM和基于字符的N-gram模型的三种监督机器学习算法，对美国和欧洲七个热门旅游目的地的旅游博客上的评论进行了情感分类。经验结果表明，SVM和N-gram方法优于Naieve Bayes方法，并且当训练数据集具有大量评论时，所有这三种方法的准确性至少达到80％。

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