{"title":"Classification of Emoji Categories from Tweet Based on Deep Neural Networks","authors":"Kazuyuki Matsumoto, Minoru Yoshida, K. Kita","doi":"10.1145/3278293.3278306","DOIUrl":null,"url":null,"abstract":"In this paper, we describe the sentiment analysis method from tweets based on emoji's category. Many of existing study about sentiment analysis focused on the emotional expressions included in sentence. However, because there are various kinds of emotional expressions, such as Internet slang, it cannot be constructed that the fixed emotional expression dictionary. The most of the methods based on corpus and machine learning, its performance is quite depended on the quality of annotation. Therefore, we attempt to use categories which are expressed by emoji as sentiment label instead of manually annotated labels. Our proposed method uses automatically annotated category label by emoji which is annotated to sentence, and train word embedding feature by deep neural networks. As the result of the experiment, our proposed method overcome the simple word feature based method.","PeriodicalId":183745,"journal":{"name":"Proceedings of the 2nd International Conference on Natural Language Processing and Information Retrieval","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2nd International Conference on Natural Language Processing and Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3278293.3278306","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
In this paper, we describe the sentiment analysis method from tweets based on emoji's category. Many of existing study about sentiment analysis focused on the emotional expressions included in sentence. However, because there are various kinds of emotional expressions, such as Internet slang, it cannot be constructed that the fixed emotional expression dictionary. The most of the methods based on corpus and machine learning, its performance is quite depended on the quality of annotation. Therefore, we attempt to use categories which are expressed by emoji as sentiment label instead of manually annotated labels. Our proposed method uses automatically annotated category label by emoji which is annotated to sentence, and train word embedding feature by deep neural networks. As the result of the experiment, our proposed method overcome the simple word feature based method.