社交网络舆情分析是一种新的研究趋势,而其中微博话题的情感倾向性判定是社交网络舆情分析中的热点。针对微博内容特征以及微博间转发、评论关系特征,构建情感分析用词典、网络用语词典以及表情符号库,设计基于短语路径的微博话题情感倾向性判定算法,以及基于多特征的微博话题情感倾向性判定算法,并进一步利用微博间的转发和评论关系对基于多特征的微博话题情感倾向性判定算法进行优化,其微平均正确率与F值分别达到85.3%和79.4%。
Abstract
Public opinion analysis for micro-blog post is a new trend, wherein sentiment orientation identification on micro-blog topic is a hot issue. According to the features of contents and the various relations of Chinese micro-blog post, we construct the dictionaries of sentiment words, internet slang and emotions respectively, Then we implement the sentiment analysis algorithms based on phrase path and the multi-feature of sentiment orientation of micro-blog topics. Using micro-blogs forwarding and commentaries, we take a future step to optimize the algorithm based on the multiple features. According to the experimental results, the values of the Precision and F-measure reach 85.3% and 79.4%, respectively.
关键词
微博 /
微博话题 /
情感分析 /
观点分析 /
情感倾向性
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Key words
Micro-blog post /
Micro-blog topic /
sentiment analysis /
opinion analysis /
sentiment orientation
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脚注
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基金
国家重点基础研究发展计划(973计划)(2013CB329605);国家自然科学基金(61132009);国家科技支撑计划(2012BAH14F06)
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