A novel framework for social web forums' thread ranking based on semantics and post quality features

摘要

Online discussion forums are a valuable source of knowledge. Users may share or exchange ideas by posting content in the form of questions and answers. With the increasing volume of online content in the form of forums, finding relevant information in forums can be a challenging task and knowledge management and quality assurance of this content are of critical importance. Although online discussion forums offer search services, in most cases only keyword search is provided. In keyword search techniques, such as cosine similarity, lexical overlap between query and document terms is considered; however, these techniques do not consider the context or meaning of the terms, thus failed to retrieve the relevant documents. Earlier content-based research efforts for improving the performance of thread retrieval were primarily based on cosine similarity technique. Cosine similarity technique assigns term-weights based on term-frequency and inverse-document frequency; however, this techniqocument retrieval. To address these issues, we have proposed two thread ranking techniques for onlineue does not consider discussion semantics which may lead to less effective d discussion forums: (1) threads are ranked on the basis of a semantic similarity score between posts and (2) threads are ranked based on their participants' reputation and posts' quality. The proposed work provides a performance comparison between semantic similarity techniques and cosine similarity techniques along with reputation and post quality features in thread ranking process. Experimental results obtained using a real online forum dataset demonstrate that the proposed techniques have significantly improved thread ranking performance.