EMOTIONS IN TEXT: DIMENSIONAL AND CATEGORICAL MODELS

This project explores the application of sophisticated topic models to determine the ideal amount of subjects and evaluate them across several datasets pertaining to trends and current events. We propose a comprehensive approach that leverages the topic models such as Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), Latent Semantic Analysis (LSA), Probabilistic Latent Dirichlet Allocation (PLDA) and Probabilistic Latent Semantic Indexing (PLSI) to accurately identify andanalyse topics. Our approach utilizes various topic modelling techniques to enhance the robustness and precision of topic detection. The results demonstrate the effectiveness of topic models in capturing nuanced trends and themes within complex datasets. A user-friendly frontend, built with Flask and SQLite, enables interactive input and visualization of topic modeling results, offering an accessible platformfor real-time topic analysis and insights. Thismethodology provides a powerful tool for understanding and responding to emerging trends in dynamic datasets

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.

Breast cancer remains a critical global health concern, requiring advanced and reliable diagnostic methods for early detection and effective intervention. This work introduces an integrated ensemble framework that combines multiple dimensionality reduction (DR) techniques, including Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), and Singular Value Decomposition (SVD), with robust machine learning (ML) classifiers for improved breast cancer detection. The publicly available Wisconsin Breast Cancer Dataset (WBCD) was utilized, with rigorous data preprocessing performed to address missing values, anomalies, and class imbalance through stratified sampling and median imputation. To mitigate overfitting and underfitting, dimensionality reduction was coupled with cross-validation and ensemble strategies. The predictive performance of Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Multi-Layer Perceptron (MLP) was systematically evaluated. Experimental results show that SVM consistently achieves a maximum accuracy of 97.9 % across all applied DR techniques, while MLP and LR also reach 97.9 % accuracy with PCA and NMF, though MLP exhibits performance variability depending on the selected DR method. The findings provide practical guidance for healthcare practitioners and researchers, supporting the adoption of explainable and scalable AI-driven diagnostic tools. Limitations include the reliance on a single dataset and the need for further validation on larger and more diverse clinical cohorts. Future work will focus on enhancing model interpretability, external validation, and real-world deployment in resource-constrained settings.

Common works on emotion expressing robots are theoretically based on a dimensional (continuous) model of emotions. Nevertheless, performance tests, which are used to evaluate the emotion expressing robots, are based on categorical (discrete) models of emotions. In this paper the use of dimensional-based tests is suggested, e.g. semantic differential approaches like the pleasure-arousal-dominance-model. By deriving the test from the theory on which the design of an object is based, the validity of the test raises significantly. Major benefits are explicit guidelines for design improvement and the possible integration of arbitrary actuated expressive features for which no common framework as, for example, the facial action coding system (FACS) exists. For illustration purposes, a comparative evaluation study of the robot EDDIE is conducted: one test is based on a categorical model and one test is based on a dimensional model of emotion. A third study based on a dimensional model demonstrates the evaluation of the influence of animal like features on the perceived emotion state.

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.

Unsupervised texture classification: Automatically discover and classify texture patterns

Social tags inherent in online music services such as Last.fm provide a rich source of information on musical moods. The abundance of social tags makes this data highly beneficial for developing techniques to manage and retrieve mood information, and enables study of the relationships between music content and mood representations with data substantially larger than that available for conventional emotion research. However, no systematic assessment has been done on the accuracy of social tags and derived semantic models at capturing mood information in music. We propose a novel technique called Affective Circumplex Transformation (ACT) for representing the moods of music tracks in an interpretable and robust fashion based on semantic computing of social tags and research in emotion modeling. We validate the technique by predicting listener ratings of moods in music tracks, and compare the results to prediction with the Vector Space Model (VSM), Singular Value Decomposition (SVD), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA). The results show that ACT consistently outperforms the baseline techniques, and its performance is robust against a low number of track-level mood tags. The results give validity and analytical insights for harnessing millions of music tracks and associated mood data available through social tags in application development.

Non-negative Matrix Factorization (NMF) and Probabilistic Latent Semantic Analysis (PLSA) are two widely used methods for non-negative data decomposition of two-way data (e.g., document-term matric...

Non-negative matrix factorization (NMF) has been one of the most popular methods for feature learning in the field of machine learning and computer vision. Most existing works directly apply NMF on high-dimensional image datasets for computing the effective representation of the raw images. However, in fact, the common essential information of a given class of images is hidden in their low rank parts. For obtaining an effective low-rank data representation, we in this paper propose a non-negative low-rank matrix factorization (NLMF) method for image clustering. For the purpose of improving its robustness for the data in a manifold structure, we further propose a graph regularized NLMF by incorporating the manifold structure information into our proposed objective function. Finally, we develop an efficient alternating iterative algorithm to learn the low-dimensional representation of low-rank parts of images for clustering. Alternatively, we also incorporate robust principal component analysis into our proposed scheme. Experimental results on four image datasets reveal that our proposed methods outperform four representative methods.

Low-rank matrix factorization problems such as non negative matrix factorization (NMF) can be categorized as a clustering or dimension reduction technique. The latter denotes techniques designed to find representations of some high dimensional dataset in a lower dimensional manifold without a significant loss of information. If such a representation exists, the features ought to contain the most relevant features of the dataset. Many linear dimensionality reduction techniques can be formulated as a matrix factorization. In this paper, we combine the conjugate gradient (CG) method with the Barzilai and Borwein (BB) gradient method, and propose a BB scaling CG method for NMF problems. The new method does not require to compute and store matrices associated with Hessian of the objective functions. Moreover, adopting a suitable BB step size along with a proper nonmonotone strategy which comes by the size of convex parameter ηk, results in a new algorithm that can significantly improve the CPU time, efficiency, the number of function evaluation. Convergence result is established and numerical comparisons of methods on both synthetic and real-world datasets show that the proposed method is efficient in comparison with existing methods and demonstrate the superiority of our algorithms.

A majorization–minimization based solution to penalized nonnegative matrix factorization with orthogonal regularization

Automatic generic document summarization based on non-negative matrix factorization

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.

Normalized dimensionality reduction using nonnegative matrix factorization

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.