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Paper Abstract and Keywords
Presentation 2015-01-26 13:55
Sentiment Analysis in Twitter for Multiple Topics -- How to Detect the Polarity of Tweets Regardless of Their Topic --
Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.) ASN2014-122
Abstract (in Japanese) (See Japanese page) 
(in English) Being one of the biggest web destinations for people to express their opinions, share their experience and report real-time events, Twitter is attracting significant interests from the research community over the past few years. Tweets, the short messages posted on the Twitter website, have been subject to contextual processing by many researchers for opinion mining and sentiment analysis. Opinion mining and sentiment analysis refer to searching throughout a huge amount of data in the digital space and automatically identify and aggregate attitudes and opinions expressed by users. This is of a great use when it comes to summarizing opinions and reviews, and providing statistics and primary analysis of the level of satisfaction of customers about a product or a service. However, due to the limitation in terms of characters (i.e. 140 characters per tweet) and the use of informal language, applying the conventional techniques to extract sentiments and opinions from a tweet is not applicable, and the accuracy of the classification is in general lower than that when they are applied on long texts (e.g. reviews collected from movie review websites). On the other hand, classifying texts dealing with various topics or themes remains a challenging task: texts dealing with different topics use different lexicons, and some vocabularies might have different meanings depending on the context. Therefore, most of the existing approaches usually classify tweets dealing with one topic. In this report we propose a new method for sentiment analysis that overcomes this limitation and classifies tweets regardless of their topic. We provide a new set of features to be used by machine learning algorithms to classify tweets belonging to different topics. We analyze the importance of each of the proposed features and its contribution to enhance the accuracy of the classification.
Keyword (in Japanese) (See Japanese page) 
(in English) Twitter / sentiment analysis / machine learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 418, ASN2014-122, pp. 91-96, Jan. 2015.
Paper # ASN2014-122 
Date of Issue 2015-01-19 (ASN) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee MICT ASN MoNA  
Conference Date 2015-01-26 - 2015-01-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Nanki Shirahama 
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 2015-01-MICT-ASN-MoNA 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Sentiment Analysis in Twitter for Multiple Topics 
Sub Title (in English) How to Detect the Polarity of Tweets Regardless of Their Topic 
Keyword(1) Twitter  
Keyword(2) sentiment analysis  
Keyword(3) machine 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.)
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Date Time 2015-01-26 13:55:00 
Presentation Time 25 
Registration for ASN 
Paper # IEICE-ASN2014-122 
Volume (vol) IEICE-114 
Number (no) no.418 
Page pp.91-96 
#Pages IEICE-6 
Date of Issue IEICE-ASN-2015-01-19 

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