| Title |
Reputation Analysis of Document Using Probabilistic Latent Semantic Analysis Based on Weighting Distinctions |
| Authors |
조시원(Cho, Shi-Won) ; 이동욱(Lee, Dong-Wook) |
| Keywords |
PLSA ; Reputation analysis ; EM-Algorithm |
| Abstract |
Probabilistic Latent Semantic Analysis has many applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. In this paper, we propose an algorithm using weighted Probabilistic Latent Semantic Analysis Model to find the contextual phrases and opinions from documents. The traditional keyword search is unable to find the semantic relations of phrases, Overcoming these obstacles requires the development of techniques for automatically classifying semantic relations of phrases. Through experiments, we show that the proposed algorithm works well to discover semantic relations of phrases and presents the semantic relations of phrases to the vector-space model. The proposed algorithm is able to perform a variety of analyses, including such as document classification, online reputation, and collaborative recommendation. |