Finding Thoughtful Comments from Social Media

• Main Authors: GOTTIPATI Swapna, Jing JIANG
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Opinion mining; Information Extraction; Text Classification; Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/3240; https://ink.library.smu.edu.sg/context/sis_research/article/4242/viewcontent/8904582.pdf
• Institution: Singapore Management University

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.