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Paper Abstract and Keywords
Presentation 2016-01-29 09:50
Multi-Class Sentiment Analysis in Twitter: a Pattern-Based Approach
Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.) ASN2015-89
Abstract (in Japanese) (See Japanese page) 
(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) 
(in English) Sentiment Analysis / Twitter / Opinion Mining / Machine Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 437, ASN2015-89, pp. 57-62, Jan. 2016.
Paper # ASN2015-89 
Date of Issue 2016-01-21 (ASN) 
ISSN Print edition: ISSN 0913-5685    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)
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Conference Information
Committee MICT ASN MoNA  
Conference Date 2016-01-28 - 2016-01-29 
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 
Paper Information
Registration To ASN 
Conference Code 2016-01-MICT-ASN-MoNA 
Language English 
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 Analysis  
Keyword(2) Twitter  
Keyword(3) Opinion Mining  
Keyword(4) Machine Learning  
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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.)
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Speaker Author-1 
Date Time 2016-01-29 09:50:00 
Presentation Time 25 minutes 
Registration for ASN 
Paper # ASN2015-89 
Volume (vol) vol.115 
Number (no) no.437 
Page pp.57-62 
#Pages 6 
Date of Issue 2016-01-21 (ASN) 

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