基于Word2Vec的情感词典充实方法

Eissa Alshari, A. Azman, S. Doraisamy, N. Mustapha, Mostafa Alkeshr
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引用次数: 38

摘要

近年来,许多研究人员对使用词汇词典进行情感分析产生了兴趣。SentiWordNet是最常用的情感词汇来确定文本的极性。然而,由于维数的诅咒,语料库词汇中有大量术语不在SentiWordNet中,这将限制情感分析的性能。本文提出了一种基于SentiWordNet的通过学习词汇中非意见词的极性来扩大意见词大小的方法。利用互联网电影评论数据集对该方法的有效性进行了评价。结果表明,所提出的Senti2Vec方法可以比SentiWordNet更有效地作为情感词汇资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effective Method for Sentiment Lexical Dictionary Enrichment Based on Word2Vec for Sentiment Analysis
Recently, many researchers have shown interest in using lexical dictionary for sentiment analysis. The SentiWordNet is the most used sentiment lexical to determine the polarity of texts. However, there are huge number of terms in the corpus vocabulary that are not in the SentiWordNet due to the curse of dimensionality, which will limit the performance of the sentiment analysis. This paper proposed a method to enlarge the size of opinion words by learning the polarity of those non-opinion words in the vocabulary based on the SentiWordNet. The effectiveness of the method is evaluated by using the Internet Movie Review Dataset. The result is promising, showing that the proposed Senti2Vec method can be more effective than the SentiWordNet as the sentiment lexical resource.
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