IEICE TRANSACTIONS on Information
Incorporation of Target Specific Knowledge for Sentiment Analysis on Microblogging
Summary :
Sentiment analysis of microblogging has become an important classification task because a large amount of user-generated content is published on the Internet. In Twitter, it is common that a user expresses several sentiments in one tweet. Therefore, it is important to classify the polarity not of the whole tweet but of a specific target about which people express their opinions. Moreover, the performance of the machine learning approach greatly depends on the domain of the training data and it is very time-consuming to manually annotate a large set of tweets for a specific domain. In this paper, we propose a method for sentiment classification at the target level by incorporating the on-target sentiment features and user-aware features into the classifier trained automatically from the data createdfor the specific target. An add-on lexicon, extended target list, and competitor list are also constructed as knowledge sources for the sentiment analysis. None of the processes in the proposed framework require manual annotation. The results of our experiment show that our method is effective and improves on the performance of sentiment classification compared to the baselines.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.4 pp.959-968
- Publication Date
- 2016/04/01
- Publicized
- 2016/01/14
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015DAP0021
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering and Information Management)
- Category
Authors
Yongyos KAEWPITAKKUN
Japan Advanced Institute of Science and Technology (JAIST)
Kiyoaki SHIRAI
Japan Advanced Institute of Science and Technology (JAIST)
Keyword
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Yongyos KAEWPITAKKUN, Kiyoaki SHIRAI, "Incorporation of Target Specific Knowledge for Sentiment Analysis on Microblogging" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 4, pp. 959-968, April 2016, doi: 10.1587/transinf.2015DAP0021.
Abstract: Sentiment analysis of microblogging has become an important classification task because a large amount of user-generated content is published on the Internet. In Twitter, it is common that a user expresses several sentiments in one tweet. Therefore, it is important to classify the polarity not of the whole tweet but of a specific target about which people express their opinions. Moreover, the performance of the machine learning approach greatly depends on the domain of the training data and it is very time-consuming to manually annotate a large set of tweets for a specific domain. In this paper, we propose a method for sentiment classification at the target level by incorporating the on-target sentiment features and user-aware features into the classifier trained automatically from the data createdfor the specific target. An add-on lexicon, extended target list, and competitor list are also constructed as knowledge sources for the sentiment analysis. None of the processes in the proposed framework require manual annotation. The results of our experiment show that our method is effective and improves on the performance of sentiment classification compared to the baselines.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015DAP0021/_p
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@ARTICLE{e99-d_4_959,
author={Yongyos KAEWPITAKKUN, Kiyoaki SHIRAI, },
journal={IEICE TRANSACTIONS on Information},
title={Incorporation of Target Specific Knowledge for Sentiment Analysis on Microblogging},
year={2016},
volume={E99-D},
number={4},
pages={959-968},
abstract={Sentiment analysis of microblogging has become an important classification task because a large amount of user-generated content is published on the Internet. In Twitter, it is common that a user expresses several sentiments in one tweet. Therefore, it is important to classify the polarity not of the whole tweet but of a specific target about which people express their opinions. Moreover, the performance of the machine learning approach greatly depends on the domain of the training data and it is very time-consuming to manually annotate a large set of tweets for a specific domain. In this paper, we propose a method for sentiment classification at the target level by incorporating the on-target sentiment features and user-aware features into the classifier trained automatically from the data createdfor the specific target. An add-on lexicon, extended target list, and competitor list are also constructed as knowledge sources for the sentiment analysis. None of the processes in the proposed framework require manual annotation. The results of our experiment show that our method is effective and improves on the performance of sentiment classification compared to the baselines.},
keywords={},
doi={10.1587/transinf.2015DAP0021},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Incorporation of Target Specific Knowledge for Sentiment Analysis on Microblogging
T2 - IEICE TRANSACTIONS on Information
SP - 959
EP - 968
AU - Yongyos KAEWPITAKKUN
AU - Kiyoaki SHIRAI
PY - 2016
DO - 10.1587/transinf.2015DAP0021
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2016
AB - Sentiment analysis of microblogging has become an important classification task because a large amount of user-generated content is published on the Internet. In Twitter, it is common that a user expresses several sentiments in one tweet. Therefore, it is important to classify the polarity not of the whole tweet but of a specific target about which people express their opinions. Moreover, the performance of the machine learning approach greatly depends on the domain of the training data and it is very time-consuming to manually annotate a large set of tweets for a specific domain. In this paper, we propose a method for sentiment classification at the target level by incorporating the on-target sentiment features and user-aware features into the classifier trained automatically from the data createdfor the specific target. An add-on lexicon, extended target list, and competitor list are also constructed as knowledge sources for the sentiment analysis. None of the processes in the proposed framework require manual annotation. The results of our experiment show that our method is effective and improves on the performance of sentiment classification compared to the baselines.
ER -
English
