Sentiment Detection of Web Users Using Probabilistic Latent Semantic Analysis

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.

Contextual text mining is concerned with extracting topical themes from a text collection with context information (e.g., time and location) and comparing/analyzing the variations of themes over different contexts. Since the topics covered in a document are usually related to the context of the document, analyzing topical themes within context can potentially reveal many interesting theme patterns. In this paper, we generalize some of these models proposed in the previous work and we propose a new general probabilistic model for contextual text mining that can cover several existing models as special cases. Specifically, we extend the probabilistic latent semantic analysis (PLSA) model by introducing context variables to model the context of a document. The proposed mixture model, called contextual probabilistic latent semantic analysis (CPLSA) model, can be applied to many interesting mining tasks, such as temporal text mining, spatiotemporal text mining, author-topic analysis, and cross-collection comparative analysis. Empirical experiments show that the proposed mixture model can discover themes and their contextual variations effectively.

Improving document clustering in a learned concept space

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.

At present, in the mainstream sentiment analysis methods represented by Support Vector Machine, the vocabulary and the latent semantic information contained in the text cannot be well considered, and sentiment analysis of text is overly dependent on the statistics of sentiment words. In this paper, a Fisher kernel function based on Probabilistic Latent Semantic Analysis is proposed for sentiment analysis by Support Vector Machine. The Fisher kernel function based on the model is derived by the Probabilistic Latent Semantic Analysis model. By means of this method, latent semantic information containing the probability features can be used as the classification features, the effect of classification for support vector machine can be improved, and the problem of not considering the latent semantic features in text sentiment analysis is solved. The results show that compared with the comparison method, the effect of the method proposed in this paper is obviously improved.

Humans can easily find a specific category of objects in a complex scene, simulating this mechanism has both scientific and economic impact. The existing research usually focuses on where humans will look, overlooking the effects of the category of objects on visual attention. In this paper, we proposed a computational visual model aimed at mimicking human visual attention while they were looking for a specific category of objects. First, we proposed a machine learning-based Probabilistic Latent Semantic Analysis (PLSA) model to recognize the generic category of objects and find their locations. The PLSA model parameters are learned in a supervised manner. Then, the category and location information of objects are used as high-level information, together with low-level features of the same objects, to train a Support Vector Regression (SVR) model. With this SVR model, we can predict the visual attention for a specific category. To demonstrate the effectiveness and applicability of the proposed method, we conducted comprehensive experiments. The results show that the model can provide a reasonable estimate for human visual attention under the visual search task for a specific category. Besides, we evaluated the proposed model's ability to discover a specific category of objects and their locations on the natural image dataset. The results show that besides facilitating the human visual attention mechanism research, the proposed model can also be applied to find a specific category of objects from a set of images.

In this study, we propose a novel salient object detection strategy based on regional contrast and relative spatial compactness. Our algorithm consists of four basic steps. First, we learn color names offline using the probabilistic latent semantic analysis (PLSA) model to find the mapping between basic color names and pixel values. The color names can be used for image segmentation and region description. Second, image pixels are assigned to special color names according to their values, forming different color clusters. The saliency measure for every cluster is evaluated by its spatial compactness relative to other clusters rather than by the intra variance of the cluster alone. Third, every cluster is divided into local regions that are described with color name descriptors. The regional contrast is evaluated by computing the color distance between different regions in the entire image. Last, the final saliency map is constructed by incorporating the color cluster's spatial compactness measure and the corresponding regional contrast. Experiments show that our algorithm outperforms several existing salient object detection methods with higher precision and better recall rates when evaluated using public datasets.

Digital pathological image retrieval plays an important role in computer-aided diagnosis for breast cancer. The retrieval results of an unknown pathological image, which are generally previous cases with diagnostic information, can provide doctors with assistance and reference. In this paper, we develop a novel pathological image retrieval method for breast cancer, which is based on stain component and probabilistic latent semantic analysis (pLSA) model. Specifically, the method firstly utilizes color deconvolution to gain the representation of different stain components for cell nuclei and cytoplasm, and then block Gabor features are conducted on cell nuclei, which is used to construct the codebook. Furthermore, the connection between the words of the codebook and the latent topics among images are modeled by pLSA. Therefore, each image can be represented by the topics and also the high-level semantic concepts of image can be described. Experiments on the pathological image database for breast cancer demonstrate the effectiveness of our method.

Activity understanding plays an essential role in video content analysis and remains a challenging open problem. Most of previous research is limited due to the use of excessively localized features without sufficiently encapsulating the interaction context or focus on simply discriminative models but totally ignoring the interaction patterns. In this paper, a new approach is proposed to recognize human group activities. Firstly, we design a new quaternion descriptor to describe the interactive insight of activities regarding the appearance, dynamic, causality and feedback, respectively. The designed descriptor is capable of delineating the individual and pairwise interactions in the activities. Secondly, considering both activity category and interaction variety, we propose an extended pLSA (probabilistic Latent Semantic Analysis) model with two hidden variables. This extended probabilistic graphic paradigm constructed on the quaternion descriptors facilitates the effective inference of activity categories as well as the exploration of activity interaction patterns. The experiments on the realistic movie and human activity databases validate that the proposed approach outperforms the state-of-the-art results.KeywordsActivity analysisgenerative modelingvideo description

In this paper, foreground detection is performed by scene interpretation. A natural scene in different illumination conditions is characterized by scene context which contains spatial and appearance representations. The spatial representation is obtained in two steps. First, the large homogenous regions in each sample image are extracted using local and global dominant color histograms (DCH). Then, the latent semantic regions of the scene are generated by combining the coincident regions in the segmented images. The appearance representation is learned by the probabilistic latent semantic analysis (PLSA) model with local DCH visual words. The scene context is then applied to interpret incoming images from the scene. For a new image, its global appearance is first recognized and then the pixels are labelled under the constraint of the scene appearance. The proposed method has been tested on various scenes under different weather conditions and very promising results have been obtained.

The proliferation of online Location-Based Social Networks (LBSN) has offered unprecedented opportunities for understanding fine-grained spatio-temporal behaviors of users and developing new location-aware applications. In this article, we focus on the problem of “Sparse User Check-in Venue Prediction,” where the goal is to predict the next venue LBSN users will visit by exploiting their sparse online check-in traces and the latent decision contexts. While efforts have been made to predict users’ check-in traces on a LBSN, several important challenges still exist. First, check-in traces contributed by LBSN users are often too sparse to provide sufficient evidence for a reliable prediction, especially when the prediction space is huge (e.g., hundreds of thousands of venues in large cities). Second, the user's decision context on which venue to visit next is often latent and has not been incorporated by current venue prediction models. Third, the dynamic and non-deterministic dependency between check-ins is either ignored or replaced by a simplified “consecutiveness” assumption in existing solutions, leading to sub-optimal prediction results. In this article, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context-aware Sparse Check-in Venue Prediction (CSCVP)</i> scheme inspired by natural language processing techniques to address the above challenges. In particular, CSCVP predicts the venue category information and explores the similarity between users to address data sparsity challenge by significantly reducing the prediction space. It also leverages the Probabilistic Latent Semantic Analysis (PLSA) model to incorporate the user decision context into the prediction model. Finally, we develop a novel Temporal Adaptive Ngram (TA-Ngram) model in CSCVP to capture the dynamic and non-deterministic dependency between check-ins. We evaluate CSCVP using three real-world LBSN datasets. The results show that our scheme significantly improves accuracy (30.9 percent improvement) of the state-of-the-art user check-in venue prediction solutions.

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.

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.

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.

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.