Publication Type

Conference Proceeding Article

Publication Date

12-2012

Abstract

Online user comments contain valuable user opinions. Comments vary greatly in quality and detecting high quality comments is a subtask of opinion mining and summarization research. Finding attentive comments that provide some reasoning is highly valuable in understanding the user’s opinion particularly in sociopolitical opinion mining and aids policy makers, social organizations or government sectors in decision making. In this paper we study the problem of detecting thoughtful comments. We empirically study various textual features, discourse relations and relevance features to predict thoughtful comments. We use logistic regression model and test on the datasets related to sociopolitical content. We found that the most useful features include the discourse relations and relevance features along with basic textual features to predict the comment quality in terms of thoughtfulness. In our experiments on two different datasets, we could achieve a prediction score of 79.37% and 73.47% in terms of F-measure on the two data sets, respectively.

Keywords

Opinion mining, Information Extraction, Text Classification

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Management and Analytics

Publication

Proceedings of COLING 2012: 24th International Conference on Computational Linguistics, 8-15 December 2012, Mumbai, India

First Page

995

Last Page

1010

ISBN

9781627483896

Publisher

ACL

City or Country

Stroudsburg, PA

Copyright Owner and License

LARC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://aclweb.org/anthology/C/C12/C12-1061.pdf

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