{"title":"利用Word2Vec和词语情感信息对孟加拉语评论进行情感分析","authors":"M. Al-Amin, Md Saiful Islam, Shapan Das Uzzal","doi":"10.1109/ECACE.2017.7912903","DOIUrl":null,"url":null,"abstract":"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%.","PeriodicalId":333370,"journal":{"name":"2017 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"62","resultStr":"{\"title\":\"Sentiment analysis of Bengali comments with Word2Vec and sentiment information of words\",\"authors\":\"M. Al-Amin, Md Saiful Islam, Shapan Das Uzzal\",\"doi\":\"10.1109/ECACE.2017.7912903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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%.\",\"PeriodicalId\":333370,\"journal\":{\"name\":\"2017 International Conference on Electrical, Computer and Communication Engineering (ECCE)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"62\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Electrical, Computer and Communication Engineering (ECCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECACE.2017.7912903\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Electrical, Computer and Communication Engineering (ECCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECACE.2017.7912903","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 62
摘要
使用word2vec模型(Mikolov et al.(2013))的孟加拉语词向量表示在孟加拉语情感分类中起着重要作用。可以观察到，来自相同上下文的单词在word2vec模型的向量空间中保持更近的距离，并且它们比其他单词更相似。本文提出了一种基于word2vec和情感提取的孟加拉语评论情感分类新方法。将word2vec词共现得分的结果与词的情感极性得分相结合，得到的准确率为75.5%。
Sentiment analysis of Bengali comments with Word2Vec and sentiment information of words
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%.
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