Conference Proceeding Article
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.
Opinion mining, Information Extraction, Text Classification
Databases and Information Systems | Social Media
Data Management and Analytics
Proceedings of COLING 2012: 24th International Conference on Computational Linguistics, 8-15 December 2012, Mumbai, India
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GOTTIPATI Swapna and Jing JIANG.
Finding Thoughtful Comments from Social Media. (2012). Proceedings of COLING 2012: 24th International Conference on Computational Linguistics, 8-15 December 2012, Mumbai, India. 995-1010. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3240
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