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Paper Abstract and Keywords
Presentation 2020-12-17 11:15
A Novel Data Augmentation Framework Based on SeqGAN for Sentiment Analysis
Jiawei Luo, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.) PRMU2020-43
Abstract (in Japanese) (See Japanese page) 
(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) 
(in English) data augmentation / sentiment analysis / machine learning / sentence compression / SeqGAN / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 300, PRMU2020-43, pp. 30-35, Dec. 2020.
Paper # PRMU2020-43 
Date of Issue 2020-12-10 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF PRMU2020-43

Conference Information
Committee PRMU  
Conference Date 2020-12-17 - 2020-12-18 
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 
Paper Information
Registration To PRMU 
Conference Code 2020-12-PRMU 
Language English 
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 augmentation  
Keyword(2) sentiment analysis  
Keyword(3) machine learning  
Keyword(4) sentence compression  
Keyword(5) SeqGAN  
Keyword(6)  
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Keyword(8)  
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.)
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Speaker Author-1 
Date Time 2020-12-17 11:15:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2020-43 
Volume (vol) vol.120 
Number (no) no.300 
Page pp.30-35 
#Pages 6 
Date of Issue 2020-12-10 (PRMU) 

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