Semi-supervised and Unsupervised Methods for Categorizing Posts in Web Discussion Forums

Abstract

Web discussion forums are used by millions of people worldwide to share information belonging to a variety of domains such as automotive vehicles, pets, sports, etc. They typically contain posts that fall into different categories such as problem, solution, feedback, spam, etc. Automatic identification of these categories can aid information retrieval that is tailored for specific user requirements. Previously, a number of supervised methods have attempted to solve this problem; however, these depend on the availability of abundant training data. A few existing unsupervised and semi-supervised approaches are either focused on identifying a single category or do not report category-specific performance. In contrast, this work proposes unsupervised and semi-supervised methods that require no or minimal training data to achieve this objective without compromising on performance. A fine-grained analysis is also carried out to discuss their limitations. The proposed methods are based on sequence models (specifically, Hidden Markov Models) that can model language for each category using word and part-of-speech probability distributions, and manually specified features. Empirical evaluations across domains demonstrate that the proposed methods are better suited for this task than existing ones.