| Presentation | 2016-01-29 Multi-Class Sentiment Analysis in Twitter: a Pattern-Based Approach Mondher Bouazizi, Tomoaki Ohtsuki, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Many works were conducted on the automatic sentiment analysis and opinion mining. However, most of these works were oriented towards the classification of texts into positive and negative. In this report, we propose a pattern-based approach that goes deeper in the classification of texts collected from Twitter (i.e., tweets) and classifies the tweets into 7 different classes. Experiments show that our approach reaches an accuracy of classification equal to 56.9% and a precision level of sentimental tweets (other than neutral and sarcastic) equal to 72.58%. Nevertheless, the approach proves to be very accurate in binary classification (i.e., classification into “positive” and “negative”) and ternary classification (i.e., classification into “positive”, “negative” and “neutral”): in the former case, we reach an accuracy of 87.5% for the same dataset used after removing neutral tweets, and in the latter case, we reached an accuracy of classification of 83.0%. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Sentiment AnalysisTwitterOpinion MiningMachine Learning |
| Paper # | ASN2015-89 |
| Date of Issue | 2016-01-21 (ASN) |
| Conference Information | |
| Committee | MICT / ASN / MoNA |
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| Conference Date | 2016/1/28(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hotel Okada |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Ambient intelligence, ICT for Medical, Healthcare and Sports, etc |
| Chair | Ryuji Kohno(Yokohama National Univ.) / Hiroshi Tohjo(NTT) / Hiroaki Morino(Shibaura Inst. of Tech.) |
| Vice Chair | Masaru Sugimachi(National Cerebral and Cardiovascular Center) / Takahiro Aoyagi(Tokyo Inst. of Tech.) / Hiroo Sekiya(Chiba Univ.) / Hiraku Okada(Nagoya Univ.) / Kiyohito Yoshihara(KDDI R&D Labs.) / Ryoichi Shinkuma(Kyoto Univ.) |
| Secretary | Masaru Sugimachi(NICT) / Takahiro Aoyagi(Nagoya Inst. of Tech.) / Hiroo Sekiya(Kanagawa Inst. of Tech.) / Hiraku Okada(NTT) / Kiyohito Yoshihara(KDDI R&D Labs.) / Ryoichi Shinkuma(Kumamoto Univ.) |
| Assistant | Kohei Ohno(Meiji Univ.) / Keisuke Shima(Yokohama National Univ.) / Tomoko Tateyama(Ritsumeikan Univ.) / Yuichi Igarashi(Hitachi) / Katsuhiro Naito(Aichi Inst. of Tech.) / Kiyohiko Hattori(NICT) / Hiroshi Fujita(Fujitsu Labs.) / Takuro Yonezawa(Keio Univ.) / Yoshifumi Morihiro(NTT DoCoMo) / Hisashi Kurasawa(NTT) / Makoto Suzuki(Univ. of Tokyo) |
| Paper Information | |
| Registration To | Technical Committee on Healthcare and Medical Information Communication Technology / Technical Committee on Ambient intelligence and Sensor Networks / Technical Committee on Mobile Network and Applications |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Multi-Class Sentiment Analysis in Twitter: a Pattern-Based Approach |
| Sub Title (in English) | |
| Keyword(1) | Sentiment AnalysisTwitterOpinion MiningMachine Learning |
| 1st Author's Name | Mondher Bouazizi |
| 1st Author's Affiliation | Keio University(Keio Univ.) |
| 2nd Author's Name | Tomoaki Ohtsuki |
| 2nd Author's Affiliation | Keio University(Keio Univ.) |
| Date | 2016-01-29 |
| Paper # | ASN2015-89 |
| Volume (vol) | vol.115 |
| Number (no) | ASN-437 |
| Page | pp.pp.57-62(ASN), |
| #Pages | 6 |
| Date of Issue | 2016-01-21 (ASN) |