Bipolar Person Name Identification of Topic Documents Using Principal Component Analysis

In this paper, we propose an unsupervised approach for identifying bipolar person names in a set of topic documents. We employ principal component analysis (PCA) to discover bipolar word usage patterns of person names in the documents and show that the signs of the entries in the principal eigenvector of PCA partition the person names into bipolar groups spontaneously. Empirical evaluations demonstrate the efficacy of the proposed approach in identifying bipolar person names of topics.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Chien Chin Chen, Chen-Yuan Wu
• Keywords: Bipolar Person Names, Bipolar Word Usage, COLING 2010, Computational Linguistics, Principal Component Analysis