使用Twitter数据的产品评论情感极性分类

Dileep Kumar Boyapati, Jagathi Gottipati, Vinod Kattula, S. Yelisetti
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引用次数: 0

摘要

情感分析,通常被称为意见挖掘,揭示了消费者对特定商品或服务的态度和感受。情感极性分类是情感分析的基本问题,它确定评论是有利的、消极的还是中性的。还有一些研究空白,因为一些研究只调查了积极、中性和消极的情绪类别;这些研究都没有考虑超过三个类别;此外,这些研究都没有考虑到情绪极性方面的个人和综合影响。之前的方法没有考虑到动词、副词、形容词及其组合,以及五种情绪类别和三种情绪极性特征。本研究提供了一种基于情感对即时视频在线评论进行分类的方法。拟议的研究利用了50万条互联网评论的大量数据集。此外,还考虑了形容词、动词和两个极性特征,以及它们与各种感官的配对。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment Polarity Categorization of Product Reviews using Twitter Data
Sentiment analysis, commonly referred to as opinion mining, reveals the attitudes and feelings of consumers about specific goods or services. The sentiment polarity classification, which identifies whether a review is favourable, negative, or neutral, is the fundamental issue with sentiment analysis. There are still some study gaps, as some studies only investigate the positive, neutral, and negative sentiment classes; none of these studies considered more than three classes; also, none of these studies considered the individual and combined effects of the sentiment polarity aspects. No prior method took into account the verb, adverb, adjective, and their combinations, as well as the five sentiment classes and three sentiment polarity traits. This study, provides a method for categorizing online reviews of Instant Videos based on their sentiment. Proposed study makes use of a substantial data set of 500,000 internet reviews. This review-level categorization process Adjective, verb, and two polarity traits are taken into account additionally as well as their pairings with various senses.
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