Mining User Relations from Online Discussions using Sentiment Analysis and Probabilistic Matrix Factorization
Minghui Qiu, Liu Yang and Jing Jiang
Advances in sentiment analysis have enabled extraction of user relations
implied in online textual exchanges such as forum posts. However, recent
studies in this direction only consider direct relation extraction from text.
As user interactions can be sparse in online discussions, we propose to apply
collaborative filtering through probabilistic matrix factorization to
generalize and improve the opinion matrices extracted from forum posts.
Experiments with two tasks show that the learned latent factor representation
can give good performance on a relation polarity prediction task and improve
the performance of a subgroup detection task.
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