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Paper Abstract and Keywords
Presentation 2023-09-06 15:30
Rating Prediction of Multi-aspect Reviews Using Multi-task Learning Framework
Masaki Takeo, Shinnosuke Kawasaki, Kazutaka Shimada (Kyutech) NLC2023-4
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a method for rating prediction tasks in review documents.
Existing models for predicting the rating scores assigned to each aspect were often developed separately.
However, there is a relationship among aspects.
Therefore, we apply a multi-task learning framework to our prediction models.
We name this method ``Simultaneous Learning.''
We evaluate our model with a review dataset about a game software domain.
Each review document contains six-level rating scores for seven aspects of the game software.
We utilize BERT as the base model for the prediction.
Our model simultaneously learns the parameters of seven BERTs for seven aspects.
In addition, we compare two types of input data for the rating prediction task: all sentences and selected sentences.
Experimental results show the effectiveness of our simultaneous learning model in the multi-aspect rating prediction.
Keyword (in Japanese) (See Japanese page) 
(in English) Sentiment Analysis / Review Analysis / Simultaneous Learning / Parameter Sharing / Rating Predicition / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 176, NLC2023-4, pp. 18-23, Sept. 2023.
Paper # NLC2023-4 
Date of Issue 2023-08-30 (NLC) 
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 NLC2023-4

Conference Information
Committee NLC  
Conference Date 2023-09-06 - 2023-09-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Osaka Metropolitan University. Nakamozu Campus. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 20th Text Analytics Symposium 
Paper Information
Registration To NLC 
Conference Code 2023-09-NLC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Rating Prediction of Multi-aspect Reviews Using Multi-task Learning Framework 
Sub Title (in English)  
Keyword(1) Sentiment Analysis  
Keyword(2) Review Analysis  
Keyword(3) Simultaneous Learning  
Keyword(4) Parameter Sharing  
Keyword(5) Rating Predicition  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Masaki Takeo  
1st Author's Affiliation Kyushu Institute of Technology (Kyutech)
2nd Author's Name Shinnosuke Kawasaki  
2nd Author's Affiliation Kyushu Institute of Technology (Kyutech)
3rd Author's Name Kazutaka Shimada  
3rd Author's Affiliation Kyushu Institute of Technology (Kyutech)
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Speaker Author-1 
Date Time 2023-09-06 15:30:00 
Presentation Time 25 minutes 
Registration for NLC 
Paper # NLC2023-4 
Volume (vol) vol.123 
Number (no) no.176 
Page pp.18-23 
#Pages 6 
Date of Issue 2023-08-30 (NLC) 

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