Probabilistic Latent Semantic Analyses (PLSA) in Bibliometric Analysis for Technology Forecasting

Wang Zan, Y.C. TSIM, W.S. Yeung, K.C. Chan, Jinlan Liu

Abstract


Due to the availability of internet-based abstract services and patent databases, bibliometric analysis has become one of key technology forecasting approaches. Recently, latent semantic analysis (LSA) has been applied to improve the accuracy in document clustering. In this paper, a new LSA method, probabilistic latent semantic analysis (PLSA) which uses probabilistic methods and algebra to search latent space in the corpus is further applied in document clustering. The results show that PLSA is more accurate than LSA and the improved iteration method proposed by authors can simplify the computing process and improve the computing efficiency

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