Predicting Subjectivity Orientation of Online Forum Threads

Online forums contain huge amounts of valuable information in the form of discussions between forum users. The topics of discussions can be subjective seeking opinions of other users on some issue or non-subjective seeking factual answer to specific questions. Internet users search these forums for different types of information such as opinions, evaluations, speculations, facts, etc. Hence, knowing subjectivity orientation of forum threads would improve information search in online forums. In this paper, we study methods to analyze subjectivity of online forum threads. We build binary classifiers on textual features extracted from thread content to classify threads as subjective or non-subjective. We demonstrate the effectiveness of our methods on two popular online forums.

[1]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[2]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[3]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[4]  Eugene Agichtein,et al.  CoCQA: Co-Training over Questions and Answers with an Application to Predicting Question Subjectivity Orientation , 2008, EMNLP.

[5]  Ellen Riloff,et al.  Learning Extraction Patterns for Subjective Expressions , 2003, EMNLP.

[6]  Noriko Kando,et al.  Multi-Document Summarization with Subjectivity Analysis at DUC 2005 , 2005 .

[7]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[8]  Jackie Chi Kit Cheung,et al.  Multi-Document Summarization of Evaluative Text , 2013, EACL.

[9]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[10]  Cornelia Caragea,et al.  I want what i need!: analyzing subjectivity of online forum threads , 2012, CIKM.

[11]  Jon Atli Benediktsson,et al.  Multiple Classifier Systems , 2015, Lecture Notes in Computer Science.

[12]  W. Bruce Croft,et al.  Online community search using thread structure , 2009, CIKM.

[13]  Nitin Indurkhya,et al.  Handbook of Natural Language Processing , 2010 .

[14]  Min-Yen Kan,et al.  Product review summarization from a deeper perspective , 2011, JCDL '11.

[15]  Tetsuya Sakai,et al.  Community QA Question Classification: Is the Asker Looking for Subjective Answers or Not? , 2011 .

[16]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[17]  Hong Yu,et al.  Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences , 2003, EMNLP.

[18]  Eugene Agichtein,et al.  Exploring question subjectivity prediction in community QA , 2008, SIGIR '08.

[19]  Cornelia Caragea,et al.  Thread Specific Features are Helpful for Identifying Subjectivity Orientation of Online Forum Threads , 2012, COLING.

[20]  Iryna Gurevych,et al.  Educational Question Answering based on Social Media Content , 2009, AIED.

[21]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[22]  Janyce Wiebe,et al.  Recognizing subjectivity: a case study in manual tagging , 1999, Natural Language Engineering.

[23]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[24]  Claire Cardie,et al.  Multi-Perspective Question Answering Using the OpQA Corpus , 2005, HLT.

[25]  Giuseppe Carenini,et al.  Interactive multimedia summaries of evaluative text , 2006, IUI '06.

[26]  Swapna Somasundaran,et al.  QA with Attitude: Exploiting Opinion Type Analysis for Improving Question Answering in On-line Discussions and the News , 2007, ICWSM.

[27]  Prasenjit Mitra,et al.  Adopting Inference Networks for Online Thread Retrieval , 2010, AAAI.