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A topic is usually associated with a specific time, place, and person(s). Generally, topics that involve bipolar or competing viewpoints are attention getting and are thus reported in a large number of documents. Identifying the association between important persons mentioned in numerous topic documents would help readers comprehend topics more easily. In this paper, we propose an unsupervised approach for identifying bipolar person names in a set of topic documents. Specifically, 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. To reduce the effect of data sparseness, we introduce two techniques, called the weighted correlation coefficient and off-topic block elimination. We also present a timeline system that shows the intensity and activeness development of the identified bipolar person groups. Empirical evaluations demonstrate the efficacy of the proposed approach in identifying bipolar person names in topic documents, while the generated timelines provide comprehensive storylines of topics.