Using non-lexical features for identifying factual and opinionative threads in online forums
Subjectivity analysis essentially deals with separating factual information and opinionative information. It has been actively used in various applications such as opinion mining of customer reviews in online review sites, improving answering of opinion questions in community questionanswering (CQA) sites, multi-document summarization, etc. However, there has not been much focus on subjectivity analysis in the domain of online forums. Online forums contain huge amounts of user-generated data in the form of discussions between forum members on specific topics and are a valuable source of information. In this work, we perform subjectivity analysis of online forum threads. We model the task as a binary classification of threads in one of the two classes: subjective (seeking opinions, emotions, other private states) and non-subjective (seeking factual information). Unlike previous works on subjectivity analysis, we use several non-lexical thread-specific features for identifying subjectivity orientation of threads. We evaluate our methods by comparing them with several state-of-the-art subjectivity analysis techniques. Experimental results on two popular online forums demonstrate that our methods outperform strong baselines in most of the cases.
online forums subjectivity analysis classification dialog act supervised learning