Probabilistic Latent Semantic Analysis (PLSA) is a novel statistical technique for analyzing two-mode and co-occurrence data, with applications in information retrieval, natural language processing, and machine learning from text. Unlike standard Latent Semantic Analysis (LSA), which uses Singular Value Decomposition (SVD) of co-occurrence tables, PLSA is based on a mixture decomposition derived from a latent class model, providing a more principled and statistically sound approach. To avoid overfitting, PLSA employs a generalized maximum likelihood model fitting method called Tempered Expectation Maximization (TEM). The paper introduces the aspect model, a statistical model for co-occurrence data that associates an unobserved class variable with each observation. This model defines a proper generative model of the data, offering advantages such as a well-defined probability distribution and interpretable directions in the latent space. PLSA is compared to LSA in terms of computational complexity and performance, showing that PLSA can achieve significant improvements in perplexity and average precision in various experiments. Experimental results demonstrate that PLSA outperforms LSA in tasks such as perplexity minimization and automated indexing of documents. The use of TEM further enhances the generalization of PLSA, leading to substantial performance gains. Overall, PLSA is presented as a promising unsupervised learning method with broad applicability in text learning and information retrieval.Probabilistic Latent Semantic Analysis (PLSA) is a novel statistical technique for analyzing two-mode and co-occurrence data, with applications in information retrieval, natural language processing, and machine learning from text. Unlike standard Latent Semantic Analysis (LSA), which uses Singular Value Decomposition (SVD) of co-occurrence tables, PLSA is based on a mixture decomposition derived from a latent class model, providing a more principled and statistically sound approach. To avoid overfitting, PLSA employs a generalized maximum likelihood model fitting method called Tempered Expectation Maximization (TEM). The paper introduces the aspect model, a statistical model for co-occurrence data that associates an unobserved class variable with each observation. This model defines a proper generative model of the data, offering advantages such as a well-defined probability distribution and interpretable directions in the latent space. PLSA is compared to LSA in terms of computational complexity and performance, showing that PLSA can achieve significant improvements in perplexity and average precision in various experiments. Experimental results demonstrate that PLSA outperforms LSA in tasks such as perplexity minimization and automated indexing of documents. The use of TEM further enhances the generalization of PLSA, leading to substantial performance gains. Overall, PLSA is presented as a promising unsupervised learning method with broad applicability in text learning and information retrieval.