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| Presentation | 2016-05-12 10:50 [Encouragement Talk] A Novel Approach for Multi-Class Sentiment Analysis in Twitter Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.)  ASN2016-3 |
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| 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 72.6%. 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) | Twitter / sentiment analysis / opinion mining / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 22, ASN2016-3, pp. 13-18, May 2016. | |
| Paper # | ASN2016-3 | |
| Date of Issue | 2016-05-05 (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) | |
| Download PDF | ASN2016-3 |
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| Conference Information | |
| Committee | ASN |
| Conference Date | 2016-05-12 - 2016-05-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | ASN |
| Conference Code | 2016-05-ASN |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Novel Approach for Multi-Class Sentiment Analysis in Twitter |
| Sub Title (in English) | |
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| Keyword(2) | sentiment analysis |
| Keyword(3) | opinion mining |
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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 | 2016-05-12 10:50:00 |
| Presentation Time | 25 minutes |
| Registration for | ASN |
| Paper # | ASN2016-3 |
| Volume (vol) | vol.116 |
| Number (no) | no.22 |
| Page | pp.13-18 |
| #Pages | 6 |
| Date of Issue | 2016-05-05 (ASN) |
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