Sentiment Analysis of Bengali Comments With Word2Vec and Sentiment Information of Words

被引：0

作者：

Al-Amin, Md. ^{[1
]}

Islam, Md. Saiful ^{[1
]}

Das Uzzal, Shapan ^{[1
]}

机构：

[1] Shahjalal Univ Sci & Technol, Dept CSE, Sylhet, Bangladesh

来源：

2017 INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER AND COMMUNICATION ENGINEERING (ECCE) | 2017年

关键词：

Word Embedding; Word2Vec; Sentiment Polarity; Valence Shifter; CBOW; Skip-gram;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The vector representation of Bengali words using word2vec model (Mikolov et al. (2013)) plays an important role in Bengali sentiment classification. It is observed that the words that are from same context stay closer in the vector space of word2vec model and they are more similar than other words. In this article, a new approach of sentiment classification of Bengali comments with word2vec and Sentiment extraction of words are presented. Combining the results of word2vec word co-occurrence score with the sentiment polarity score of the words, the accuracy obtained is 75.5%.

引用

页码：186 / 190

页数：5

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