PPLSA: Parallel Probabilistic Latent Semantic Analysis Based on MapReduce

A novel method is proposed for updating an already trained asymmetric and symmetric probabilistic latent semantic analysis (PLSA) model within the context of a varying document stream. The proposed method is coined online PLSA (oPLSA). The oPLSA employs a fixed-size moving window over a document stream to incorporate new documents and at the same time to discard old ones (i.e., documents that fall outside the scope of the window). In addition, the oPLSA assimilates new words that had not been previously seen (out-of-vocabulary words), and discards the words that exclusively appear in the documents to be thrown away. To handle the new words, Good-Turing estimates for the probabilities of unseen words are exploited. The experimental results demonstrate the superiority in terms of accuracy of the oPLSA over well known PLSA updating methods, such as the PLSA folding-in (PLSA fold.), the PLSA rerun from the breakpoint, the quasi-Bayes PLSA, and the Incremental PLSA. A comparison with respect to the CPU run time reveals that the oPLSA is the second fastest method after the PLSA fold. However, the better accuracy of the oPLSA than that of the PLSA fold. pays off the longer computation time. The oPLSA and the other PLSA updating methods together with online LDA are tested for document clustering and F1 scores are also reported.

PLSA (Probabilistic Latent Semantic Analysis) is a popular topic modeling technique which has been widely applied to text mining applications to discover the underlying topics embedded in the data corpus. However, due to the variability of increasing data, it is necessary to discover the dynamic topics and process the large dataset incrementally. Moreover, PLSA models suffer from the problem of inferencing new documents. To overcome these problems, in this paper, we propose a novel Weighted Incremental PLSA algorithm called WIPLSA to dynamically discover topics and incrementally learn the topics from new documents. The experiments verify that the proposed WIPLSA could capture the dynamic topics hidden in the dynamic updating data corpus. Compared with PLSA, MAP PLSA and QB PLSA, WIPLSA performs better in perspexity on large dataset, which make it applicable for big data mining. In addition, WIPLSA has good performance in the application of document categorization.

Probabilistic Latent Semantic Analysis (PLSA) is one of the latent topic models and it has been successfully applied to visual recognition tasks. However, PLSA models have been learned mainly in batch learning, which can not handle data that arrives sequentially. In this paper, we propose a novel on-line learning algorithm for learning the parameters of PLSA. Our contributions are two-fold: (i) an on-line learning algorithm that learns the parameters of a PLSA model from incoming data; (ii) a codebook adaptation algorithm that can capture the full characteristics of all the features during the learning. Experimental results demonstrate that the proposed algorithm can handle sequentially arriving data that batch PLSA learning cannot cope with, and its performance is comparable with that of the batch PLSA learning on visual recognition.

Due to the vast growth of data collections, the statistical document modeling has become increasingly important in language processing areas. Probabilistic latent semantic analysis (PLSA) is a popular approach whereby the semantics and statistics can be effectively captured for modeling. However, PLSA is highly sensitive to task domain, which is continuously changing in real-world documents. In this paper, a novel Bayesian PLSA framework is presented. We focus on exploiting the incremental learning algorithm for solving the updating problem of new domain articles. This algorithm is developed to improve document modeling by incrementally extracting up-to-date latent semantic information to match the changing domains at run time. By adequately representing the priors of PLSA parameters using Dirichlet densities, the posterior densities belong to the same distribution so that a reproducible prior/posterior mechanism is activated for incremental learning from constantly accumulated documents. An incremental PLSA algorithm is constructed to accomplish the parameter estimation as well as the hyperparameter updating. Compared to standard PLSA using maximum likelihood estimate, the proposed approach is capable of performing dynamic document indexing and modeling. We also present the maximum a posteriori PLSA for corrective training. Experiments on information retrieval and document categorization demonstrate the superiority of using Bayesian PLSA methods.

The aim of this paper is to categorize movies into genres using the previews. Our study attempts to combine audio, visual and text features to classify a collection of movie previews into action, biography, comedy, and horror. For each of the collected previews, the audio and visual features are extracted and the text features are drawn from social tags via social websites. The probabilistic latent semantic analysis (PLSA) is used to incorporate the features from these three different aspects of information. The standard PLSA processes one type of information only. Therefore double-model and triple-model PLSAs are extended in order to combine two or three different types of information. We compare these various variants of PLSA approaches with unimodal PLSAs, which use either audio, visual or text features only. The experimental results show not only that one of the triple-model PLSAs achieves the highest accuracy, but also that social tags (text features) play an important role for classifying movies genres.

In today's digital era, social media data provides valuable insights into public opinion. This study implements the Graph Regularized Probabilistic Latent Semantic Analysis (GPLSA) method to analyze topics from social media data surrounding the 2024 Indonesian Presidential Election (Pemilu), as well as to evaluate the efficiency of the Probabilistic Latent Semantic Analysis (PLSA) algorithm. The research stages include collecting social media data on presidential debates and elections, text pre-processing, and applying the GPLSA method to identify main topics. The analysis results show that PLSA without graph achieved a topic coherence score of 0.653, indicating good consistency, while GPLSA decreased to 0.5, suggesting that the addition of graph regularization did not significantly enhance coherence. Additionally, PLSA without graph achieved a perplexity score of 12.138, indicating good predictive capability, while GPLSA increased to 12.511, showing that graph regularization did not improve the prediction of new words. PLSA without graph also produced topics relevant to election issues, while GPLSA generated topics influenced by graph regularization, though without significant improvement in topic quality. Sentiment analysis of social media posts provides insights into public responses to debates and election issues. Validation of the GPLSA model ensures relevant topic representation. This research contributes to the development of text analysis methods and offers valuable information for elections and democratic participation. These results can be utilized by stakeholders to make more strategic and informed decisions.

With the wide application of Internet in almost all fields, it has become the most important way for information publication, providing a large number of channels for spreading public opinion. Public opinions, as the response of Internet users to the information such as social events and government policies, reflect the status of both society and economics, which is highly valuable for the decision-making and public relations of enterprises. At present, the analysis methods for Internet public opinion are mainly based on discriminative approaches, such as Support Vector Machine (SVM) and neural network. However, when these approaches analyze the sentiment of Internet public opinion, they are failed to exploit information hidden in text, e.g. topic. Motivated by the above observation, this paper proposes a detection method for public sentiment based on Probabilistic Latent Semantic Analysis (PLSA) model. PLSA inherits the advantages of LSA, exploiting the semantic topic hidden in data. The procedure of detecting the public sentiment using this algorithm is composed of three main steps: (1) Chinese word segmentation and word refinement, with which each document is represented by a bag of words; (2) modeling the probabilistic distribution of documents using PLSA; (3) using the Z-vector of PLSA as the features of documents and delivering it to SVM for sentiment detection. We collect a set of text data from Weibo, blog, BBS etc. to evaluate our proposed approach. The experimental results shows that the proposed method in this paper can detect the public sentiment with high accuracy, outperforming the state-of-the-art approaches, i.e., word histogram based approach. The results also suggest that, text semantic analysis using PLSA could significantly boost the sentiment detection.

Probabilistic Latent Semantic Analysis (PLSA) is a popular technique to analyze non-negative data where multinomial distributions underlying every data vector are expressed as linear combinations of a set of basis distributions. These learned basis distributions that characterize the dataset lie on the standard simplex and themselves represent corners of a simplex within which all data approximations lie. In this paper, we describe a novel method to extend the PLSA decomposition where the bases are not constrained to lie on the standard simplex and thus are better able to characterize the data. The locations of PLSA basis distributions on the standard simplex depend on how the dataset is aligned with respect to the standard simplex. If the directions of maximum variance of the dataset are orthogonal to the standard simplex, then the PLSA bases will give a poor representation of the dataset. Our approach overcomes this drawback by utilizing Singular Values Decomposition (SVD) to identify the directions of maximum variance, and transforming the dataset to align these directions parallel to the standard simplex before performing PLSA. The learned PLSA features are then transformed back into the data space. The effectiveness of the proposed approach is demonstrated with experiments on synthetic data.

Methods for analysis of principal components in discrete data have existed for some time under various names such as grade of membership modelling, probabilistic latent semantic analysis, and genotype inference with admixture. In this paper we explore a number of extensions to the common theory, and present some application of these methods to some common statistical tasks. We show that these methods can be interpreted as a discrete version of ICA. We develop a hierarchical version yielding components at different levels of detail, and additional techniques for Gibbs sampling. We compare the algorithms on a text prediction task using support vector machines, and to information retrieval.

As text data continues to grow quickly, it is increasingly important to develop intelligent systems to help people manage and make use of vast amounts of text data (big text data''). As a new family of effective general approaches to text data retrieval and analysis, probabilistic topic models---notably Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocations (LDA), and their many extensions---have been studied actively in the past decade with widespread applications. These topic models are powerful tools for extracting and analyzing latent topics contained in text data; they also provide a general and robust latent semantic representation of text data, thus improving many applications in information retrieval and text mining. Since they are general and robust, they can be applied to text data in any natural language and about any topics. This tutorial systematically reviews the major research progress in probabilistic topic models and discuss their applications in text retrieval and text mining. The tutorial provides (1) an in-depth explanation of the basic concepts, underlying principles, and the two basic topic models (i.e., PLSA and LDA) that have widespread applications, (2) an introduction to EM algorithms and Bayesian inference algorithms for topic models, (3) a hands-on exercise to allow the tutorial attendants to learn how to use the topic models implemented in the MeTA Open Source Toolkit and experiment with provided data sets, (4) a broad overview of all the major representative topic models that extend PLSA or LDA, and (5) a discussion of major challenges and future research directions.

Locally discriminative topic modeling

The primary goal of Web usage mining is the discovery of patterns in the navigational behavior of Web users. Standard approaches, such as clustering of user sessions and discovering association rules or frequent navigational paths, do not generally provide the ability to automatically characterize or quantify the unobservable factors that lead to common navigational patterns. It is, therefore, necessary to develop techniques that can automatically discover hidden semantic relationships among users as well as between users and Web objects. Probabilistic Latent Semantic Analysis (PLSA) is particularly useful in this context, since it can uncover latent semantic associations among users and pages based on the co-occurrence patterns of these pages in user sessions. In this paper, we develop a unified framework for the discovery and analysis of Web navigational patterns based on PLSA. We show the flexibility of this framework in characterizing various relationships among users and Web objects. Since these relationships are measured in terms of probabilities, we are able to use probabilistic inference to perform a variety of analysis tasks such as user segmentation, page classification, as well as predictive tasks such as collaborative recommendations. We demonstrate the effectiveness of our approach through experiments performed on real-world data sets.

Probabilistic latent semantic analysis (PLSA) has been widely used in the machine learning community. However, the original PLSAs are not capable of modeling real-valued observations and usually have severe problems with over fitting. To address both issues, we propose a novel, regularized Gaussian PLSA (RG-PLSA) model that combines Gaussian PLSAs and hierarchical Gaussian mixture models (HGMM). We evaluate our model on supervised human action recognition tasks, using two publicly available datasets. Average classification accuracies of 97.69% and 93.72% are achieved on the Weizmann and KTH Action Datasets, respectively, which demonstrate that the RG-PLSA model outperforms Gaussian PLSAs and HGMMs, and is comparable to the state of the art.

Topic modeling is the statistical model for discovering hidden topics or keywords in a collection of documents. Topic modeling is also considered a probabilistic model for learning, analyzing, and discovering topics from the document collection. The most popular techniques for topic modeling are latent semantic analysis (LSA), probabilistic latent semantic analysis (pLSA), latent Dirichlet allocation (LDA), and the recent deep learning-based lda2vec. LDA is most commonly used in extractive multi-document summarization to determine whether the extracted sentence reflects the concept of the input document. In this paper, we will try to explore various multi-document summarization techniques that use LDA as a topic modeling method for improving final summary coverage and to reduce redundancy. Finally, we compared LDA and LSA using the Genism toolkit, and our experiment results show that LDA outperforms LSA if we increase the number of features considered for sentence selection.KeywordsTopic modelingText summarizationLDALSA

Aviation safety analysis increasingly relies on extracting actionable insights from narrative incident reports to support risk identification and improve operational safety. Topic modeling techniques such as Probabilistic Latent Semantic Analysis (pLSA) and BERTopic offer automated methods to uncover latent themes in unstructured safety narratives. This study evaluates the effectiveness of each model in generating coherent, interpretable, and semantically meaningful topics for aviation safety practitioners and researchers. We assess model performance using both quantitative metrics (topic coherence scores) and qualitative evaluations of topic relevance. The findings show that while pLSA provides a solid probabilistic framework, BERTopic leveraging transformer-based embeddings and HDBSCAN clustering produces more nuanced, context-aware topic groupings, albeit with increased computational demands and tuning complexity. These results highlight the respective strengths and trade-offs of traditional versus modern topic modeling approaches in aviation safety analysis. This work advances the application of natural language processing (NLP) in aviation by demonstrating how topic modeling can support risk assessment, inform policy, and enhance safety outcomes.