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PMF Model for Mining User Relations
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.
This package implements the construction of opinion matrices which are the input of PMF model. The main features include aspect identification, opinion expression identification and opinion relation extraction based on dependency path rules. More details of our methods for aspect identification, opinion identification and opinion relation extraction are described in the related paper http://aclweb.org/anthology/N13-1041.
Related Publication: Qiu, M., Yang, L., & Jiang, J. (2013). Mining User Relations from Online Discussions using Sentiment Analysis and Probabilistic Matrix Factorization. NAACL HLT 2013, 401-410. http://aclweb.org/anthology/N13-1041
Available in InK: http://ink.library.smu.edu.sg/sis_research/1891/