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Sentiment Analysis of Micro-blog Integrated on Explicit Semantic Analysis Method

机译:显式语义分析方法中微博的情感分析

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摘要

Combined with existing research of short text classification, this paper analyzes and explores the structure and characteristics of Wikipedia. And the fusion of explicit semantic analysis algorithm micro-blog sentiment analysis method is proposed. Wikipedia is regarded as external semantic knowledge base, and the entries are introduced as a supplement of micro-blog text features. The author improves the previous micro-blog sentiment analysis text representation method, and then constructs the naive Bias classifier to achieve emotion classification. The experimental results show that after the introduction of Wikipedia to micro-blog text feature expansion, the final evaluation index of the classification results of the naive Bias classifier has been improved, which achieves a better classification effect and improves the effectiveness of the sentiment classification.
机译:结合现有的短文本分类研究,本文分析并探讨了维基百科的结构和特征。 并提出了显式语义分析算法的融合微博情绪分析方法。 维基百科被视为外部语义知识库,并将条目作为微博文本特征的补充。 作者提高了以前的微博语言分析文本表示方法,然后构建天真偏置分类器以实现情绪分类。 实验结果表明,在将维基百科引入微博文本的特征扩展后,已经提高了天真偏置分类器的分类结果的最终评估指标,这实现了更好的分类效果并提高了情绪分类的有效性。

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