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Sentiment Analysis of Hindi Review based on Negation and Discourse Relation

机译:基于否定和话语关系的印地语评论情感分析

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With recent developments in web technologies, percentage of web content in Hindi language is growing up at a lightning speed. Opinion classification research has gained tremendous momentum in recent times mostly for English language. However, there has been little work in this area for Indian languages. There is a need to analyse the Hindi language content and get insight of opinions expressed by people and various communities. In this paper, a method is proposed to increase the coverage of the Hindi SentiWordNet for better classification results. In addition to this, impact of the negation and discourse rules are investigated for Hindi sentiment analysis. Proposed algorithm produces 82.89% for positive reviews and 76.59 % for negative reviews, and an overall accuracy of 80.21%.
机译:随着网络技术的最新发展,印地语语言中网络内容的百分比正以惊人的速度增长。最近,意见分类研究获得了巨大的发展势头,主要是英语。但是,印度语言在该领域几乎没有工作。有必要分析印地语的语言内容,并了解人们和各个社区表达的观点。本文提出了一种增加印地语SentiWordNet覆盖率的方法,以获得更好的分类结果。除此之外,还对否定和话语规则的影响进行了调查,以进行印地语情感分析。提出的算法产生的正面评价为82.89%,负面评价为76.59%,总体准确度为80.21%。

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