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Automatic construction of domain sentiment lexicon for semantic disambiguation
Multimedia Tools and Applications ( IF 3.6 ) Pub Date : 2020-05-22 , DOI: 10.1007/s11042-020-09030-1
Yanyan Wang , Fulian Yin , Jianbo Liu , Marco Tosato

Sentiment lexicon is used to judge the sentiments of words and plays a significant role in sentiment analysis. Existing sentiment lexicons ignore the sentimental ambiguity of words in different contexts and only assign sentiment positive or negative polarity for words. In this paper, we propose an automatic method for the construction of the domain-specific sentiment lexicon (SDS-lex) to avoid sentimental ambiguity, which incorporates the sentiment information not only from the existing lexicons but also from the corpus by using our improved TF-IDF algorithm (ITF-IDF). The ITF-IDF algorithm calculates the sentiment of words by considering both the importance of words and the distribution of different part-of-speech (POS) in a corpus labeled with different sentiment tendencies. Experiments on real-world datasets show that our constructed lexicon improves the sentimental ambiguity and outperforms many existing lexicons in terms of the coverage and the accuracy when performing text sentiment classification tasks.



中文翻译:

语义歧义化领域情感词典的自动构建

情感词典用于判断单词的情感,并在情感分析中起重要作用。现有的情感词典在不同上下文中忽略了单词的情感歧义,只为单词分配了情感的正极性或负极性。在本文中,我们提出了一种自动构建领域特定情感词典(SDS-lex)的方法,以避免情感歧义,该方法不仅使用现有词典中的情感信息,而且还使用改进后的TF融合了语料库中的情感信息。 -IDF算法(ITF-IDF)。ITF-IDF算法通过同时考虑单词的重要性和标有不同情感倾向的语料库中不同词性(POS)的分布来计算单词的情感。

更新日期:2020-05-22
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