Probabilistic Latent Semantic Analysis (PLSA) - Complete Implementation Package

Probabilistic Latent Semantic Analysis (PLSA) is a fundamental text mining technique designed to uncover latent semantic structures within document collections. PLSA efficiently processes large-scale text data to discover hidden relationships between documents, making it applicable to information retrieval, text classification, and text clustering domains. The complete implementation package encompasses multiple stages including data preprocessing, feature extraction, model training, and result analysis. During data preprocessing, PLSA first performs text cleaning and tokenization, removing irrelevant information and converting text into vector representations using techniques like bag-of-words or TF-IDF. In the feature extraction phase, PLSA calculates document similarity based on vector representations to identify underlying semantic structures through techniques such as cosine similarity measurement. The core implementation involves the EM (Expectation-Maximization) algorithm for model training, which iteratively estimates the probabilities of latent topics given documents and words. Key functions include calculating the E-step (posterior probability estimation) and M-step (parameter maximization) to optimize the likelihood function. Finally, through model training and result analysis, PLSA generates latent semantic representations of texts, providing support for subsequent data analysis and applications. The implementation typically includes visualization components for topic distributions and document clustering results.