基于词向量的领域情感词典构建

doi:10.6040/j.issn.1672-3961.0.2017.403

山东大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 40-47.doi: 10.6040/j.issn.1672-3961.0.2017.403

基于词向量的领域情感词典构建

林江豪^1,2,周咏梅^1,2*,阳爱民^1,2,陈锦^1,3

1. 广东外语外贸大学语言工程与计算实验室, 广东广州 510006;2. 广东外语外贸大学信息科学与技术学院, 广东广州 510006;3. 广东外语外贸大学国际学院, 广东广州 510420

收稿日期:2017-08-23 出版日期:2018-06-20 发布日期:2017-08-23
通讯作者: 周咏梅(1971— ),女,湖南永州人,硕士,教授,主要研究领域为文本情感分析,机器学习等. E-mail:yongmeizhou@163.com E-mail:lin_hao@foxmail.com
作者简介:林江豪(1985— ),男,广东揭阳人,硕士,助理研究员,主要研究领域为自然语言处理,文本情感分析. E-mail:lin_hao@foxmail.com
基金资助:
教育部人文社会科学资助项目(14YJA740011);广东省教育厅科技创新资助项目(2013KJCX0067);广东省哲学社会科学“十二五”规划资助项目(GD15YTS01);广东省科技计划资助项目(2017A040406025);广东外语外贸大学教改资助项目(GWJY2017046)

Building of domain sentiment lexicon based on word2vec

LIN Jianghao^1,2, ZHOU Yongmei^1,2*, YANG Aimin^1,2, CHEN Jin^1,3

1. Laboratory for Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510006, Guangdong, China;
2. School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou 510006, Guangdong, China;
3. International College, Guangdong University of Foreign Studies, Guangzhou 510420, Guangdong, China

Received:2017-08-23 Online:2018-06-20 Published:2017-08-23

摘要/Abstract

摘要： 针对现有领域情感词典在情感和语义表达等方面的不足,提出一种基于词向量的领域情感词典构建方法。利用25万篇新闻语料和10万余条酒店评论数据,训练得到word2vec模型;选择80个情感明显、内容丰富、词性多样化的情感词作为种子词集;利用TF-IDF值在词汇重要程度的度量作用,在酒店评论中获得9 860个领域候选情感词汇;通过计算候选情感词与种子词的词向量之间的语义相似度,将情感词映射到高维向量空间,实现了情感词的特征向量表示(Senti2vec)。将Senti2vec应用于情感词极性分类和文本情感分析任务中,试验结果表明,Senti2vec能实现情感词的语义表示和情感表示;基于特定领域语料的语义相似计算,使得提取的情感特征更具有领域特性,同时不受候选情感词集范围的约束。

关键词: word2vec, 情感词, 语义相似度, 情感特征向量, 领域情感词典

Abstract: In order to fill the gap of sentimental and semantic representation in domain sentiment lexicon, a construction method of domain sentiment lexicon via word vectors was proposed. The word2vec model was trained based on 250 thousand news texts and 100 thousand hotel review texts. Eighty sentimental words, which possed obvious sentiment, rich content and diverse POS, were chosen as a set of seed words. Meanwhile, 9 860 candidate sentimental words among the hotel review texts were acquired via the measuring value of TR-IDF. The semantic similarity between the candidate sentimental words and the seed words was calculated based on their word vectors, and the sentimental words were mapped to the high dimensional vector space and the feature vector representation(Senti2vec)was extracted. Senti2vec was applied into the polarity classification of sentimental words and sentimental text analysis. The experimental results showed that Senti2vec could represent the meaning and sentiment of sentimental words. Senti2vec was based on semantic similarity calculation from data of specific domain, which enabled this method more adaptable into different domains.

Key words: word2vec, sentiment word, sentimental feature vector, semantic similarity, domain sentiment lexicon

中图分类号:

TP391.1

林江豪,周咏梅,阳爱民,陈锦. 基于词向量的领域情感词典构建[J]. 山东大学学报(工学版), 2018, 48(3): 40-47.

LIN Jianghao, ZHOU Yongmei, YANG Aimin, CHEN Jin. Building of domain sentiment lexicon based on word2vec[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 40-47.

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基于词向量的领域情感词典构建

Building of domain sentiment lexicon based on word2vec

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