Reviews Analysis Based on Sentence and Word Relevance

Shibo Zhang, Yun Sha, Xiaojie Wang
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Abstract

Assuming that one sentence in review expresses one opinion, LDA based on sentence is performed to analysis the massive online reviews. When computing the topic's word, word relevance measure is designed which penalizes the word frequency by a factor that captures how much the word is shared across topics, words for topics can been selected more accurately. Experiments on massive review crawled from network show that the result of analyzing is better than the standard LDA, there is clearer topic cue, and recognition is improved among the topics.
基于句子和单词相关性的评论分析
假设评论中的一句话表达一种观点,采用基于句子的LDA对大量的在线评论进行分析。在计算主题词时,设计了词相关性测量,通过捕获词在主题间共享的程度来惩罚词的频率,可以更准确地选择主题词。对从网络中抓取的海量评论进行了实验,结果表明,该方法的分析结果优于标准的LDA,主题线索更清晰,主题之间的识别能力得到了提高。
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