大会名称 |
---|
2019年 総合大会 |
大会コ-ド |
2019G |
開催年 |
2019 |
発行日 |
2019-03-05 |
セッション番号 |
DS-1 |
セッション名 |
COMP 学生シンポジウム |
講演日 |
2019/03/19 |
講演場所(会議室等) |
54号館 102教室 |
講演番号 |
DS-1-4 |
タイトル |
Multi-Class Sentiment Analysis in Twitter Using Machine Learning and Deep Learning |
著者名 |
○Mondher Bouazizi, Tomoaki Ohtsuki, |
キーワード |
Twitter, Machine Learning, Deep Learning, Sentiment Analysis |
抄録 |
Multi-Class sentiment analysis is a particular type of sentiment analysis in which a piece of text is attributed one out of many sentiment classes. This is in contrast to the conventional binary or ternary sentiment analysis where the piece of text is attributed a class out of two or three, respectively. In this report, we introduce an approach that uses both deep learning (DL) and machine learning (ML) techniques to perform multi-class sentiment analysis and improve the classification accuracy compared to the approaches, which rely solely on ML or DL. For 7 different sentiment classes, our approach reaches an accuracy equal to 66.2%, outperforming the ones that rely on ML and DL by around 6% and 1% respectively. |
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