基于无监督方法的旅游领域意见极性分类

Mahima Goyal, Vishal Bhatnagar
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引用次数: 37

摘要

近年来电子商务网站的发展为用户在这些门户网站上发表自己的意见铺平了道路,这反过来又使客户在购买任何产品或服务之前查看这些评论。综合阅读这些大量的评论是繁琐而累人的。本文的目的是对旅游领域的评论进行分析,以确定该文件是正面的还是负面的。传统方法使用机器学习方法,但作者使用基于无监督字典的方法对意见进行分类。这些观点的得分是用Sentiwordnet提取出来的,这是一个流行的情感计算词典。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Polarity of Opinions Using Unsupervised Approach in Tourism Domain
The recent growth of e-commerce websites has paved a way for the users to express their opinions on these web portals which, in turn, makes the customers review these comments before buying any product or service. The comprehensive reading of these large number of reviews is cumbersome and tiring. The purpose of this paper is to perform the analysis on the tourism domain reviews to decide whether the document is positive or negative. The traditional methods use a machine learning approach, but the authors are using an unsupervised dictionary based approach to classify the opinions. The scores of the opinions are extracted using Sentiwordnet, a popular dictionary for calculating the sentiment.
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