This paper introduces Probabilistic Latent Semantic Analysis (PLSA), a statistical method for factor analysis of binary and count data, closely related to Latent Semantic Analysis (LSA). Unlike LSA, which uses Singular Value Decomposition (SVD) on co-occurrence tables, PLSA employs a generative latent class model for probabilistic mixture decomposition, offering a more principled approach grounded in statistical inference. The paper presents perplexity results for various text and linguistic data collections and discusses an application in automated document indexing. Experiments show that PLSA significantly outperforms standard LSA in terms of performance and robustness. PLSA is based on a statistical model called the aspect model, which associates an unobserved class variable with each word occurrence. This allows for the identification of different contexts of word usage without relying on dictionaries or thesauri. PLSA is particularly effective in handling polysemy (words with multiple meanings) and revealing topical similarities by grouping words that share common contexts. The paper compares PLSA with LSA, highlighting key differences in their approaches. While LSA relies on linear algebra and SVD, PLSA uses a probabilistic model with a generative data model, allowing for more accurate and interpretable results. PLSA also benefits from the use of the EM algorithm with temperature control, which helps in avoiding overfitting and improving generalization. The paper presents experimental results showing that PLSA outperforms LSA in terms of perplexity and information retrieval performance. It demonstrates that PLSA can achieve significant improvements in performance, even in cases where LSA fails. The results also show that combining PLSA models can lead to even better performance. The paper concludes that PLSA offers a more principled and robust approach to unsupervised learning, with a solid statistical foundation and the ability to handle complex data structures. The use of tempered EM and probabilistic models provides a powerful framework for text analysis and information retrieval.This paper introduces Probabilistic Latent Semantic Analysis (PLSA), a statistical method for factor analysis of binary and count data, closely related to Latent Semantic Analysis (LSA). Unlike LSA, which uses Singular Value Decomposition (SVD) on co-occurrence tables, PLSA employs a generative latent class model for probabilistic mixture decomposition, offering a more principled approach grounded in statistical inference. The paper presents perplexity results for various text and linguistic data collections and discusses an application in automated document indexing. Experiments show that PLSA significantly outperforms standard LSA in terms of performance and robustness. PLSA is based on a statistical model called the aspect model, which associates an unobserved class variable with each word occurrence. This allows for the identification of different contexts of word usage without relying on dictionaries or thesauri. PLSA is particularly effective in handling polysemy (words with multiple meanings) and revealing topical similarities by grouping words that share common contexts. The paper compares PLSA with LSA, highlighting key differences in their approaches. While LSA relies on linear algebra and SVD, PLSA uses a probabilistic model with a generative data model, allowing for more accurate and interpretable results. PLSA also benefits from the use of the EM algorithm with temperature control, which helps in avoiding overfitting and improving generalization. The paper presents experimental results showing that PLSA outperforms LSA in terms of perplexity and information retrieval performance. It demonstrates that PLSA can achieve significant improvements in performance, even in cases where LSA fails. The results also show that combining PLSA models can lead to even better performance. The paper concludes that PLSA offers a more principled and robust approach to unsupervised learning, with a solid statistical foundation and the ability to handle complex data structures. The use of tempered EM and probabilistic models provides a powerful framework for text analysis and information retrieval.