基于支持向量机的Twitter数据情感分类

Sheeba Naz, Aditi Sharan, Nidhi Malik
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引用次数: 45

摘要

Twitter的情感分析确实引起了研究领域的兴趣。Twitter中的情感分类是根据tweet的情感极性来分析tweet。该方法利用机器学习领域的分类模型,利用推特数据的不同文本特征(即n-grams)进行推特情感分类。此外,我们还使用了三种不同的加权方案来理解加权对分类器精度的影响。此外,利用tweet的情感得分向量提供外部知识,以提高SVM分类器的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment Classification on Twitter Data Using Support Vector Machine
Sentiment analysis in Twitter has really engaged interest in field of research. Sentiment classification in Twitter deals with analyzing the tweets in terms of their sentiment polarity. The proposed method deals with twitter sentiment classification by employing a classification model of machine learning domain which makes use of different textual features viz. n-grams of twitter data. Also, we have used three different weighting schemes to understand the impact of weighting on classifier accuracy. Furthermore, a sentiment score vector of tweets is used to provide external knowledge in order to improve the performance of SVM classifier.
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