| Presentation | 2020-12-17 A Novel Data Augmentation Framework Based on SeqGAN for Sentiment Analysis Jiawei Luo, Mondher Bouazizi, Tomoaki Ohtsuki, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Sentiment analysis is an important field in Natural Language Processing (NLP). It can analyze people's sentiment through their articles. On a related topic, machine learning has achieved high accuracy in sentiment analysis. However, it requires a large amount of high-quality training data that are hard to be collected. In this work, a novel data augmentation framework based on sequence generative adversarial networks (SeqGAN) is proposed to improve the sentiment analysis accuracy. In our framework, we conduct sentence compression and use a sentiment dictionary to retain the sentiment words for compressed data. The compressed data are used to train SeqGAN. We use the trained SeqGAN to generate artificial data for sentiment analysis. A classifier is used to discard generated data that may contain incorrect sentiment information. The results show that the proposed data augmentation framework helps SeqGAN generate high quality and novel text data. The data generated by the proposed framework improve the accuracy of the sentiment analysis classifier on some of the benchmark sentiment analysis dataset available. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | data augmentationsentiment analysismachine learningsentence compressionSeqGAN |
| Paper # | PRMU2020-43 |
| Date of Issue | 2020-12-10 (PRMU) |
| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2020/12/17(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Transfer learning and few shot learning |
| Chair | Yoichi Sato(Univ. of Tokyo) |
| Vice Chair | Akisato Kimura(NTT) / Masakazu Iwamura(Osaka Pref. Univ.) |
| Secretary | Akisato Kimura(Mobility Technologies) / Masakazu Iwamura(Chubu Univ.) |
| Assistant | Takashi Shibata(NTT) / Masashi Nishiyama(Tottori Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Pattern Recognition and Media Understanding |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Novel Data Augmentation Framework Based on SeqGAN for Sentiment Analysis |
| Sub Title (in English) | |
| Keyword(1) | data augmentationsentiment analysismachine learningsentence compressionSeqGAN |
| 1st Author's Name | Jiawei Luo |
| 1st Author's Affiliation | Keio University(Keio Univ.) |
| 2nd Author's Name | Mondher Bouazizi |
| 2nd Author's Affiliation | Keio University(Keio Univ.) |
| 3rd Author's Name | Tomoaki Ohtsuki |
| 3rd Author's Affiliation | Keio University(Keio Univ.) |
| Date | 2020-12-17 |
| Paper # | PRMU2020-43 |
| Volume (vol) | vol.120 |
| Number (no) | PRMU-300 |
| Page | pp.pp.30-35(PRMU), |
| #Pages | 6 |
| Date of Issue | 2020-12-10 (PRMU) |