微博数据情感分析

Di Li, J. Niu, Meikang Qiu, Meiqin Liu
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引用次数: 4

摘要

随着互联网的发展,人们在网络社交网站上分享自己的情感状态或态度,导致数据规模爆炸式增长。挖掘这些数据背后的情感信息,有助于人们了解民意和社会趋势。本文提出了一种适用于微博数据的情感分析算法。考虑到微博通常较短,使用LDA模型基于语义信息生成文本特征,而不是基于文本结构。为了确定情感的极性和程度,这里使用了SVR模型。实验表明,该算法对微博数据处理效果良好。
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Sentiment analysis on Weibo data
With the development of the Internet, people share their emotion statuses or attitudes on online social websites, leading to an explosive rise on the scale of data. Mining sentiment information behind these data helps people know about public opinions and social trends. In this paper a sentiment analysis algorithm adapting to Weibo (Microblog) data is proposed. Given that a Weibo post is usually short, LDA model is used to generate text features based on semantic information instead of text structure. To decide the sentiment polar and degree, SVR model is used here. Experiment shows the algorithm performs well on Weibo data.
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