文章基本信息

标题：Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis
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作者：AAMERA Z.H.KHAN ; Dr. MOHAMMAD ATIQUE ; Dr. V. M. THAKARE 等
期刊名称：International Journal of Electronics, Communication and Soft Computing Science and Engineering
印刷版ISSN：2277-9477
出版年度：2015
卷号：4
期号：Special 3
出版社：IJECSCSE
摘要：Sentiment analysis is a growing area of re search with significant applications in both industry and academia. Most of proposed solutions centered around supervised, and machine learning approaches. Twitter's unique characteristics give rise to new problems for current sentiment analysis methods, w hich originally focused on large opinionated corpora such as product reviews. This paper presents a new entity - level sentiment analysis method for Twitter. The method first adopts a lexicon based approach to perform entity - level sentiment analysis. This me thod can give high precision, but low recall. To improve recall, additional tweets that are likely to be opinionated are identified automatically by exploiting the information in the result of the lexicon - based method. A classifier is then trained to assig n polarities to the entities in the newly identified tweets
关键词：Sentiment analysis; machine learning; twitter