SORM: A Social Opinion Relevance Model

摘要

This paper presents a generic and domain independent opinion relevance model for a Social Network user. The Social Opinion Relevance Model (SORM) is able to estimate an opinion's relevance based on twelve different parameters. Compared to other models, SORM's main distinction is its ability to provide customized results according to whom the opinion relevance is being estimated for. Due to the lack of opinion relevance corpuses able to properly test our model, we have created a new one called Social Opinion Relevance Corpus (SORC). Using SORC, we carried out some experiments on the Electronic Games domain that illustrate the importance of the customizing the opinion relevance in order to achieve better results on typical Information Retrieval metrics, such as NDCG, QMeasure and MAP. We also performed a statistical significance test that reinforces and corroborates the advantages that SORM offers.