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The understanding and analyzing of available content on Social media Platforms such as Twitter and Facebook, through various topic modeling methods is not supervised. However, despite several existing conventional techniques, they have had limited success when applied directly for filtering and quick comprehension of short-text contents due to text sparseness and noise. Thus, it always has been challenging to discover reliable latent topics from online discussion texts that prevail with low words co-occurrence and availability of large size social media benchmark datasets, even for resource-rich languages. The existing literature lacks such work for Urdu text to unveil niche topics even with conventional topic models, mainly due to the lack of benchmark datasets, limited availability of pre-processing tools/ algorithms, and time and compute limitations on large-sized datasets. This work presents experiments with multiple approaches of topic modeling like Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF) on 0.8 million Urdu tweets. These tweets are collected through Twitter API by giving various hashtags as a query to avoid dominance of single topic in the dataset. In addition, we have pre-processed the text of the tweets, prepared the three variants of the collected dataset, and extracted multiple features to represent documents on different n-grams. Furthermore, all these techniques are compared and evaluated on the dataset variants, using both qualitative and quantitative measures. We have also demonstrated the results of these approaches through visualization methods, graphs depicting tweets size per topic, word clouds, and hashtags analysis, giving insights about algorithms performances on finalized topics. Observed results reveal that NMF outperformed the techniques with TF-IDF feature vectors in Urdu tweets text, while LDA performed best with merging short-text strategy into long pseudo documents.

Scene classification is one of the most fundamental task in interpretation of high-resolution remote sensing (HRRS) images. Many recent works show that the probabilistic topic models which are capable of mining latent semantics of images can be effectively applied to HRRS scene classification. However, the existing approaches based on topic models simply utilize low-level hand-crafted features to form semantic features, which severely limit the representative capability of the semantic features derived from topic models. To alleviate this problem, this paper propose to build powerful semantic features using the probabilistic latent semantic analysis (pLSA) model, by employing the pre-trained deep convolutional neural networks (CNNs) as feature extractors rather than relying on the hand-crafted features. Specifically, we develop two methods to generate semantic features, called multi-scale deep semantic representation (MSDS) and multi-level deep semantic representation (MLDS), by extracting CNN features from different layers: (1) in MSDS, the final semantic features are learned by the pLSA with multi-scale features extracted from the convolutional layer of a pre-trained CNN; (2) in MLDS, we extract CNN features for densely sampled image patches at different size level from the fully-connected layer of a pre-trained CNN, and concatenate the sematic features learned by the pLSA at each level. We comprehensively evaluate the two methods on two public HRRS scene datasets, and achieve significant performance improvement over the state-of-the-art. The outstanding results demonstrate that the pLSA model is capable of discovering considerably discriminative semantic features from the deep CNN features.

Розглядається дослідження можливостей застосування латентного семантичного аналізу для задач ідентифікації предметних наукових просторів та виявлення повноти висвітлення результатів дисертаційних досліджень здобувачами наукового ступеня.Побудовано ймовірнісну тематичну модель, яка дозволяє виконати кластеризацію публікацій науковців за науковими напрямами з врахуванням мережі цитування, що є важливим кроком для розв’язування задачі ідентифікації предметних наукових просторів. В результаті побудови моделі вирішена проблема зростання нестабільності кластеризації графу цитування у зв’язку зі зменшенням кількості кластерів. Ця проблема виникає при об'єднанні кластерів, побудованих на основі кластеризації графу цитування з врахуванням подібності анотацій наукових публікацій.Описано представлення текстових документів на основі ймовірнісної тематичної моделі з використанням n-грамів. Побудовано ймовірнісну тематичну модель для задачі встановлення повноти висвітлення матеріалів дисертаційних досліджень автора в його наукових публікаціях. Розраховано наближені значення порогових коефіцієнтів, які визначають чи враховані в статтях автора положення, які відображені в тексті автореферату дисертації. Було проведене навчання ймовірнісної тематичної моделі за публікаціями автора на основі інструменту BigARTM. Використовуючи навчену модель та з використанням спеціального регуляризатора було знайдено матрицю належності тем, які визначаються сегментами авторефератів дисертації автора до документів, які визначаються публікаціями автора.Pозглянуті важливі аспекти можливостей застосування латентного семантичного аналізу для задач ідентифікації предметних наукових просторів та виявлення повноти висвітлення результатів дисертаційних досліджень здобувачами наукового ступеня, The study considers the possibilities of using latent semantic analysis for the tasks of identifying scientific subject spaces and evaluating the completeness of covering the results of dissertation research by science degree seekers.A probabilistic thematic model was built to make it possible to cluster the publications of scholars in scientific areas, taking into account the citation network, which was an important step for solving the problem of identifying scientific subject spaces. As a result of constructing the model, the problem of increasing instability of clustering the citation graph in connection with a decrease in the number of clusters was solved. This problem would arise when combining clusters built on the basis of citation graph clustering, taking into account the similarity of abstracts of scientific publications.In the article, the presentation of text documents is described based on a probabilistic thematic model using n-grams. A probabilistic thematic model was built for the task of determining the completeness of covering the materials of an author’s dissertation research in scientific publications. The approximate values of the threshold coefficients were calculated to evaluate whether the articles of an author included the research provisions that were reflected in the text of the author’s abstract of the dissertation. The probabilistic thematic model for an author’s publications was practised on the basis of the BigARTM tool. Using the constructed model and with the help of a special regularizer, a matrix was found to evaluate the relevance of topics specified by the segments of an author’s dissertation abstracts to documents that are produced by the author’s publications.Important aspects of the possibilities of using latent semantic analysis were studied to identify tasks of scientific subject spaces and to reveal the completeness of covering the results of dissertation research science degree seekers., Рассматривается исследование возможностей применения латентного семантического анализа для задач идентификации предметных научных пространств и выявления полноты освещения результатов диссертационных исследований соискателями ученой степени.Построена вероятностная тематическая модель, которая позволяет выполнить кластеризацию публикаций ученых по научным направлениям с учетом сети цитирования, что является важным шагом для решения задачи идентификации предметных научных пространств. В результате построения модели решена проблема роста нестабильности кластеризации графа цитирования в связи с уменьшением количества кластеров. Эта проблема возникает при объединении кластеров, построенных на основе кластеризации графа цитирования с учетом сходства аннотаций научных публикаций.Описаны представления текстовых документов на основе вероятностной тематической модели с использованием n-граммов. Построена вероятностная тематическая модель для задачи определения полноты освещения материалов диссертационных исследований автора в его научных публикациях. Рассчитаны приближенные значения пороговых коэффициентов, определяющих то, учтены ли в статьях автора положения, которые отражены в тексте автореферата диссертации. Было проведено обучение вероятностной тематической модели по публикациям автора на основе инструмента BigARTM. Используя обученную модель и с использованием специального регуляризатора была найдена матрица принадлежности тем, которые определяются сегментами авторефератов диссертации автора к документам, которые определяются публикациями автора.Pоссмотрены важные аспекты возможностей применения латентного семантического анализа для задач идентификации предметных научных пространств и выявления полноты освещения результатов диссертационных исследований соискателями ученой степени

A pedestrian tracking system on highly accurate laser scanners is an effective method to understand the usage of the facility space. While this system is capable of gathering an enormous volume of tracking data, specialized skills and significant amounts of labor are needed to get a reliable bird’s-eye view of the spatio-temporal characteristics of the observed data. In this paper, two methods to extract patterns of spatio-temporal activity are described. These can provide a broad overview of the office-worker’s activities in the office throughout a workday and an easily under-stood visualization that indicates what time segment, what location and what activities are taking place. One is a time segment extraction model that identifies characteristic time intervals in the time series data of office-worker’s activities using a classification model based on information loss minimization model. The other is a day scene extraction model that identifies daily scenes from simultaneous behavior patterns in spatio-temporal distributions using a latent class model with PLSI (Probabilistic latent semantic indexing). These methods provide viewpoints for separating their activities of a workday into time segments of appropriate size in order to obtain a grasp of how the activities vary with the time of day. Simultaneous behavior patterns in time, space, and activity are extracted, thereby allowing representation of typical scenes such as morning meetings and extended conversations between co-workers.

Text classification and Topic Modelling is the backbone for the text analysis of huge amount of corpus of data. With an increase in unstructured data around us, it is very difficult to analyse the data very easily. There is a need for some methods that can be applied to the data to get the sensitive and semantic information from the corpus. Text classification is categorization of text in organised way for the interpretation of sensitive information from the text, while Topic modelling is finding the abstract topic for the collection of text or document. Topic modelling is used frequently to find semantic information from the textual data. In this paper we applied Parsing techniques on various websites to extract the HTML and XML data which includes the textual data and also applied Preprocessing techniques to clean the data. For the text classification purpose some of the Machine learning based classifiers that we have used in our experiment are Naive Bayes and also Logistic Regression Classifier. The models of the document are built using three different topic modelling methods which are Latent Semantic Analysis, Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation. In the further experiment we have done analysis and also comparison based upon the performance of the models and classifiers on the processed textual data.

Library chat services are an increasingly important communication channel to connect patrons to library resources and services. Analysis of chat transcripts could provide librarians with insights into improving services. Unfortunately, chat transcripts consist of unstructured text data, making it impractical for librarians to go beyond simple quantitative analysis (e.g., chat duration, message count, word frequencies) with existing tools. As a stepping-stone toward a more sophisticated chat transcript analysis tool, this study investigated the application of different types of topic modeling techniques to analyze one academic library’s chat reference data collected from April 10, 2015, to May 31, 2019, with the goal of extracting the most accurate and easily interpretable topics. In this study, topic accuracy and interpretability—the quality of topic outcomes—were quantitatively measured with topic coherence metrics. Additionally, qualitative accuracy and interpretability were measured by the librarian author of this paper depending on the subjective judgment on whether topics are aligned with frequently asked questions or easily inferable themes in academic library contexts. This study found that from a human’s qualitative evaluation, Probabilistic Latent Semantic Analysis (pLSA) produced more accurate and interpretable topics, which is not necessarily aligned with the findings of the quantitative evaluation with all three types of topic coherence metrics. Interestingly, the commonly used technique Latent Dirichlet Allocation (LDA) did not necessarily perform better than pLSA. Also, semi-supervised techniques with human-curated anchor words of Correlation Explanation (CorEx) or guided LDA (GuidedLDA) did not necessarily perform better than an unsupervised technique of Dirichlet Multinomial Mixture (DMM). Last, the study found that using the entire transcript, including both sides of the interaction between the library patron and the librarian, performed better than using only the initial question asked by the library patron across different techniques in increasing the quality of topic outcomes.

Short text stream clustering has become an important problem for mining textual data in diverse social media platforms (e.g., Twitter). However, most of the existing clustering methods (e.g., LDA and PLSA) are developed based on the assumption of a static corpus of long texts, while little attention has been given to short text streams. Different from the long texts, the clustering of short texts is more challenging since their word co-occurrence pattern easily suffers from a sparsity problem. In this paper, we propose a Dirichlet process biterm-based mixture model (DP-BMM), which can deal with the topic drift problem and the sparsity problem in short text stream clustering. The major advantages of DP-BMM include (1) DP-BMM explicitly exploits the word-pairs constructed from each document to enhance the word co-occurrence pattern in short texts; (2) DP-BMM can deal with the topic drift problem of short text streams naturally. Moreover, we further propose an improved algorithm of DP-BMM with forgetting property called DP-BMM-FP, which can efficiently delete biterms of outdated documents by deleting clusters of outdated batches. To perform inference, we adopt an online Gibbs sampling method for parameter estimation. Our extensive experimental results on real-world datasets show that DP-BMM and DP-BMM-FP can achieve a better performance than the state-of-the-art methods in terms of NMI metrics.

Automatic image annotation plays a significant role in image understanding, retrieval, classification, and indexing. Today, it is becoming increasingly important in order to annotate large-scale social media images from content-sharing websites and social networks. These social images are usually annotated by user-provided low-quality tags. The topic model is considered as a promising method to describe these weak-labeling images by learning latent representations of training samples. The recent annotation methods based on topic models have two shortcomings. First, they are difficult to scale to a large-scale image dataset. Second, they can not be used to online image repository because of continuous addition of new images and new tags. In this paper, we propose a novel annotation method based on topic model, namely local learning-based probabilistic latent semantic analysis (LL-PLSA), to solve the above problems. The key idea is to train a weighted topic model for a given test image on its semantic neighborhood consisting of a fixed number of semantically and visually similar images. This method can scale to a large-scale image database, as training samples involved in modeling are a few nearest neighbors rather than the entire database. Moreover, this proposed topic model, online customized for the test image, naturally addresses the issue of continuous addition of new images and new tags in a database. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms the state-of-the-art especially in terms of overall metrics.

Topic modeling is an area of natural language processing that has been actively developed in the last 15 years. A probabilistic topic model extracts a set of hidden topics from a collection of text documents. It defines each topic by a probability distribution over words and describes each document with a probability distribution over topics. The exploding volume of text data motivates the community to constantly upgrade topic modeling algorithms for multiprocessor systems. In this paper, we provide an overview of effective EM-like algorithms for learning latent Dirichlet allocation (LDA) and additively regularized topic models (ARTM). Firstly, we review 11 techniques for efficient topic modeling based on synchronous and asynchronous parallel computing, distributed data storage, streaming, batch processing, RAM optimization, and fault tolerance improvements. Secondly, we review 14 effective implementations of topic modeling algorithms proposed in the literature over the past 10 years, which use different combinations of the techniques above. Their comparison shows the lack of a perfect universal solution. All improvements described are applicable to all kinds of topic modeling algorithms: PLSA, LDA, MAP, VB, GS, and ARTM.

Ovaj rad opisuje trenutne mogućnosti i ograničenja postojećih algoritama za izdvajanju tema iz teksta. Dat je teorijski prikaz popularnih modela tema uz sve neophodne korake analize i obrade teksta koji se izvršavaju pre slanja podataka na ulaz modela. Praktičan deo rada je izdvajanje tema iz pitanja sa sajta Stack overflow. Upoređeni su LSA, PLSA i LDA pristup, a evaluacija modela je izvršena određivanjem koherent­nosti tema odgovarajućim merama, imenovanjem tema i analizom njihove vizuelizacije u prostoru. Kako modeli tema unapred zahtevaju navođenje broja tema koje će biti izdvojene iz teksta, deo rada posećen je optimizaciji hiperparametara. Izabrani model za modelovanje tema jeste LDA sa 6 tema. Da bi se dobila numerička procena performansi modela 30 pitanja je ručno označeno imenima dobijenih tema i simuliran je klasifikacioni model. Ova pitanja su korišćena kao test skup podataka u kreiranom LDA klasifikacionom modelu. Postignuta je uspešnost od 77% tačnosti.

The main objective of this research paper is to design a system which would generate multimodal, nonparametric Bayesian model, and multilayered probability latent semantic analysis (pLSA)-based visual dictionary (BM-MpLSA). Advancement in technology and the exuberance of sports lovers have necessitated a requirement for automatic action recognition in the live video seed of sports. The fundamental requirement for such model is the creation of visual dictionary for each sports domain. This multimodal nonparametric model has two novel co-occurrence matrix creation—one for image feature vector and the other for textual entities. This matrix provides a basic scaling parameter for the unobserved random variables, and it is an extension of multilayered pLSA-based visual dictionary creation. This paper precisely concentrates on the creation of visual dictionary for Basketball. From the sports event images, the feature vector extracted is modified as SIFT and MPEG 7’s-based dominant color, color layout, scalable color and edge histograms. After quantization and analysis of these vector values, the visual vocabulary would be created by integrating them into the domain specific visual ontology for semantic understanding. The accuracy rate of this work is compared with respect to the action held on image based on performance.

This letter presents a novel intersensor vegetation estimation framework, which aims at combining Sentinel-2 (S2) spatial resolution with Sentinel-3 (S3) spectral characteristics in order to generate fused vegetation maps. On the one hand, the multispectral instrument (MSI), carried by S2, provides high spatial resolution images. On the other hand, the Ocean and Land Color Instrument (OLCI), one of the instruments of S3, captures the Earth's surface at a substantially coarser spatial resolution but using smaller spectral bandwidths, which makes the OLCI data more convenient to highlight specific spectral features and motivates the development of synergetic fusion products. In this scenario, the approach presented here takes advantage of the proposed constrained probabilistic latent semantic analysis (CpLSA) model to produce intersensor vegetation estimations, which aim at synergically exploiting MSI's spatial resolution and OLCI's spectral characteristics. Initially, CpLSA is used to uncover the MSI reflectance patterns, which are able to represent the OLCI-derived vegetation. Then, the original MSI data are projected onto this higher abstraction-level representation space in order to generate a high-resolution version of the vegetation captured in the OLCI domain. Our experimental comparison, conducted using four data sets, three different regression algorithms, and two vegetation indices, reveals that the proposed framework is able to provide a competitive advantage in terms of quantitative and qualitative vegetation estimation results.

Refining image annotation has become one of the core research topics in computer vision and pattern recognition due to its great potentials in image retrieval. However, it is still in its infancy and is not sophisticated enough to extract perfect semantic concepts just according to the image low-level features. In this paper, we propose a two-stage hybrid probabilistic topic model to improve the quality of automatic image annotation. To start with, a probabilistic latent semantic analysis model with asymmetric modalities is learned to estimate the posterior probabilities of each annotation keyword, during which the image-to-word relation can be well established. Next, a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels. By this way, the information from image low-level visual features and high-level semantic concepts can be seamlessly integrated by fully taking into account the word-to-word and image-to-image relations. Finally, the rank-two relaxation heuristics is exploited to further mine the correlation of the candidate annotations so as to capture the refining results, which plays a critical role in semantic based image retrieval. Extensive experiments show that the proposed model achieves not only superior annotation accuracy but also better retrieval performance.

Automated identification of pathological retina images is very important in retinopathy. Conventional methods of retrieval in the domain of retinopathy are based on the manual observation of different components of retina. However, manual observation becomes difficult due to the large diversity of images and the varying symptoms of diseases present in pathological images. For example, varying features such as color, shape and structure of lesions complicate the manual observations and subsequent assessment of the state of the disease. As a solution to these issues, this paper proposes an unsupervised technique known as probabilistic Latent Semantic Analysis (pLSA) along with Bag of Visual Words to discriminate diseased images from normal ones. The method was tested with images from publicly available standard retina fundus image databases and achieved better performance measures compared to the existing methods. Keywords: Diabetic retinopathy, pLSA, Topic modelling, Bag of Visual Words

RATIONALE: Non-negative matrix factorization (NMF) has been used extensively for the analysis of mass spectrometry imaging (MSI) data, visualizing simultaneously the spatial and spectral distributions present in a slice of tissue. The statistical framework offers two related NMF methods: probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), which is a generative model. This work offers a mathematical comparison between NMF, PLSA, and LDA, and includes a detailed evaluation of Kullback-Leibler NMF (KL-NMF) for MSI for the first time. We will inspect their results for MSI data analysis as these different mathematical approaches impose different characteristics on the data and the resulting decomposition. METHODS: The four methods (NMF, KL-NMF, PLSA, and LDA) are compared on seven different samples: three originated from mice pancreas and four from human-lymph-node tissues, all obtained using MALDI-TOF MS. RESULTS: Where matrix factorization methods are often used for the analysis of MSI data, we find that each method has different implications on the exactness and interpretability of the results. We have discovered promising results using KL-NMF, which has only rarely been used for MSI so far, improving both NMF and PLSA, and have shown that the hitherto stated equivalent KL-NMF and PLSA algorithms do differ in case of MSI data analysis. LDA, assumed to be the better method in the field of text-mining, is shown to be outperformed by PLSA in the setting of MALDI-MSI. Additionally, the molecular results of the human-lymph-node data have been thoroughly analysed for better assessment of the methods under investigation. CONCLUSIONS: This paper offers an in-depth comparison of multiple NMF related factorization methods for MSI. We aim to provide fellow researchers in the field of MSI a clear understanding of the mathematical implications using each of these analysis techniques, which might affect the exactness and interpretation of the results. ispartof: Rapid Communications In Mass Spectrometry vol:35 issue:21 pages:e9181-e9181 ispartof: location:England status: published

FastText is a text classification model by Facebook. As the model is simple in structure, it has the advantage of fast and efficient. However, when the model is used in Chinese text classification, the accurate rate will decrease. To this end, a Chinese FastText text classification method combing Term Frequency-Relevance Frequency (TF-RF) and improved random walk model is suggested in the paper. The method makes TF-R weight choice to N-gram processed dictionaries during the input stage of the FastText model, making semantic analysis by using Probabilistic Latent Semantic Analysis (PLSA), and supplements to feature words; then utilizes the improved random walk model to improve the accuracy, and the improved model is more suitable for Chinese text classification. The experiment result shows that improved model in the paper has a better effect to Chinese text classification.

An efficient medical image classification system has gained high interest in the scientific community. This paper presents a classification algorithm that aims to gain a high accuracy rate by addressing some of the typical challenges involved in classification of large medical datasets. In this paper, the convolutional neural networks (CNNs) are employed together with probabilistic latent semantic analysis (PLSA) which are capable of mining hidden semantics of images. This high-level semantic representation of the images is then fed into a discriminative support vector machine (SVM) to build a classification model. An ensemble of machine learning models is also employed to utilize the capability of classification models created from different sets of data. The evaluation is based on a medical image dataset consisting of 11,000 X-ray images from 116 distinct categories. The classification accuracy rate obtained by the proposed classification model is 94.5 %. The results show that the proposed classification model outperformed the methods in the literature evaluated on the same benchmark dataset.

Due to the common progress and interdependence of wireless sensor networks and language, Chinese semantic analysis under wireless sensor networks has become more and more important. Although there are many research results on wireless networks and Chinese semantics, there are few researches on the influence and relationship between them. Wireless sensor networks have strong application relevance, and the key technologies that need to be solved are also different for different application backgrounds. In order to reveal the basic laws and development trends of online Chinese semantic behavior expression in the context of wireless sensor networks, this paper adopts big data analysis methods and semantic model analysis methods and constructs semantic analysis models through PLSA method calculations, so that the λ construction process conforms to this research topic. Research the accuracy and applicability of the semantic analysis model. Through word extraction of 1.05 million word data of 1,103 documents on Baidu Tieba, HowNet, and citeulike websites, the data set was integrated into a data set, and the PLSA model was verified with this data set. In addition, through the construction of the wireless sensor network, the semantic analysis results in the expression of Chinese behavior are obtained. The results show that the accuracy of the data set extracted from 1103 documents increases with the increase of the number of documents. Second, after using the PLSA model to perform semantic analysis on the data set, the accuracy of the data set is improved. Compared with traditional semantic analysis, the model and the big data analysis framework have obvious advantages. With the continuous development of Internet big data, the big data methods used to count Chinese semantics are also constantly updated, and their efficiency is constantly improving. These updated semantic analysis models and statistical methods are constantly eliminating the uncertainty of modern online Chinese. The basic laws and development trends of statistical Chinese semantics also provide new application scenarios for online Chinese behavior. It also laid a ladder for subsequent scholars.

Over the last few decades, consumers’ preferences and lifestyles have changed significantly. In such market, the number of products and services aims to mass marketing have become less effective. Therefore, personalization tailored to individual characteristics, certain segments, and one-to-one marketing focusing on individuals are becoming important. Therefore, customer relationship management, retaining existing customers and costs for acquiring new customers are important for both of academic and business field. However, for that purpose, it is essential to grasp customer behavior in more detail and use it for analysis. Therefore, in this study, we estimate the potential clusters of customers and customers’ purchase behavior. Concretely, we use pLSA and XGBoost which become popular machine learning methodologies. In this study, we set the number of store visits per month for objective variable and show a prediction model of it. Then we compare the model that incorporates the result of predicting the probability of the latent cluster as an explanatory variable with the model that incorporates a general explanatory variable.

In today's fields of education and literature, electronic commerce (e-commerce) is a well-known subject. Excellent research on e-commerce focuses predominantly on developing economies. Customer behavior analysis is gaining more prominence as consumers move from visiting retail shops to shopping online from the e-commerce view. In this article, review is taken into account in the case of sentiment analysis to evaluate, implement and analyse the most effective techniques. In numerous research ventures, including in the area of text classification and information retrieval, probabilistic models currently play a major role. Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Indexing (PLSA) and Latent Dirichlet Allocation (LDA) represent probabilistic models. The LDA approach is used in this paper to define online feedback for sentiment analysis since the LDA approach is intended to address LSA and PLSA issues. Online shopping seems to become a central platform for shoppers with the development of e-commerce, and it is necessary to forecast the scale of e-commerce transactions. This paper is used to scan online data of women's apparel. By encouraging a more custom-made shopping experience, it will boost customer loyalty and sales, resulting in higher retention rates and viable improvement.

pLSA is a useful method to know the characteristics of customer or item in marketing. In this study, we proposed a method to set the initial values more efficiently than the existing method for the problem that the final solution depends on the initial values set in the EM algorithm used by pLSA to estimate the solutions. We focused on the dimensional compression and clustering that are the characteristics of pLSA, and thought that the stability of the solution of pLSA would be improved by reflecting it in the initial values. Therefore, first, we performed correspondence analysis and k-means cluster analysis on the original data to express the features of dimensional compression and clustering. Next, we compared the performance of the pLSA results with the initial values of the proposed method and the initial values of the conventional method using random numbers. As a result, it was shown that the proposed method also converges to the same log-likelihood as the conventional method, and that the proposed method is superior in terms of convergence speed and stability.

Business processes and software components, especially class diagrams, have a firm connection. Considering software components support the business process in providing an excellent product and service. Besides, business process changes affect on software component design. One of them usually appears on the label or name of the software component or business process. Sometimes, a related business process and software component appears in the different label but the same meaning rather than using the same label. This situation is problematic when there are many changes to be made, in which the software component's modifying process becomes quite long. Therefore, the software maintainers should obtain an efficient procedure to shorten the modifying process. One solution is by using conformity checking, which helps the software maintainers know which software component is related to a specific business process. This paper compared two leading topic modeling techniques, namely probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), to determine which one has a better performance for process traceability.

in this paper, a multi-stage features sparse learning method for hyperspectral image scene recognition is proposed. The hyperspectral image is decomposed into five scales by Gaussian function at eight angles. Gabor visual features are extracted at different scales of each angle. The visual dictionary of hyperspectral image is constructed by sparse coding, and the sparse coding coefficient matrix of multi angle and multi-scale features of hyperspectral image is formed Compared with BoVW or PLSA, the overall accuracy of SVM classifier is improved by 4.47% and 5.39% respectively, and the kappa coefficient is improved by 0.0685 and 0.0768 respectively. The results show that the proposed multi-angle and multi-scale sparse coding method for hyperspectral image scene recognition is more interpretable than the description of hyperspectral image features with BoVW or PLSA, and can effectively improve the effect of hyperspectral image scene recognition.

International audience; The ontology enrichment process is text-based and the application domain in hand is circumscribed to the content of the related texts. However, the main challenge in ontology enrichment is its learning, since there is still a lack of relevant approach able to achieve automatic enrichment from a textual corpus or dataset of various topics. In this paper, we describe a new approach for automatic learning of terminological ontologies from textual corpus based on probabilistic models. In our approach, two topic modeling algorithms are explored, namely LDA and pLSA for learning topic ontology. The objective is to capture semantic relationships between word-topic and topic-document in terms of probability distributions to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and retrieving corresponding topic ontology for a user query demonstrates the effectiveness of the proposed approach.

During the study, a methodology was developed, and a software product was developed for project risk assessment based on developer communications, as well as the results of the program work on the data of the real project CASSANDRA of Apache Software Foundation. The methodology is implemented based on already well-known algorithms for determining the emotional components in the text of the VAD and matrix methods for project risk analysis using their developments that allow combining these different approaches. Obtaining the names of potential risks is performed using the model of constructing the LDA themes. The results allow us to determine the importance of the task by the communications and rank them in the middle of the project by the importance and need for additional attention that will allow project managers to understand and solve problems more quickly in the context of the product., В ходе исследования разработана методология и создан программный продукт для определения рисков проекта на базе коммуникации разработчиков, а также представлены результаты работы программы на данных реального проекта CASSANDRA компании Apache Software Foundation. Методология реализована на основе уже известных алгоритмов определения эмоциональных составляющих в тексте VAD и матричных методов анализа рисков проекта с использованием собственных разработок, позволять соединить эти разные подходы. Название потенциальных рисков определяется с помощью модели построения тем LDA. Полученные результаты позволяют определять важность задачи в соответствии с коммуникаций и ранжировать их в середине проекта по важности и необходимости дополнительного внимания, что в контексте продукта позволит менеджерам проекта более быстро понимать и решать проблемы., У ході дослідження розроблено методологію та створено програмний продукт для визначення ризиків проекту на базі комунікації розробників, подано результати роботи програми на даних реального проекту CASSANDRA компанії Apache Software Foundation. Методологію реалізовано на основі вже відомих алгоритмів визначення емоційних складових у тексті VAD та матричних методів аналізу ризиків проекту з використанням власних розробок, що дозволять об’єднати ці різні підходи. Визначення назви потенційних ризиків визначається за допомогою моделі побудови тем LDA. Отримані результати допоможуть визначати важливість задачі відповідно до комунікацій та ранжувати їх у середині проекту за важливістю та потреби додаткової уваги, що в контексті продукту дасть змогу менеджерам проекту більш швидко розуміти та вирішувати проблеми.

In the agricultural field, crop diseases and physiological disorders have a large impact on its yield and quality. Minimizing damage is very important, but most of the work such as patrols for pest control is done by humans, which is a heavy burden for farmers. Therefore, it is required to reduce the burden by using an automatic detection system for the diseases. In this paper, hyperspectral imaging and AI technology were applied to detect gray mold on tomato leaves. By using probabilistic latent semantic analysis (pLSA) and Bayesian network, optimum 8 wavelengths were selected for machine learning. The prediction accuracy using the selected wavelengths was not significantly decreased compared to using all wavelengths. The results show that pLSA can perform clustering of multidimensional spectral data without compromising the original information, and that Bayesian network is a powerful technique to visualize relationships between multiple variables.

Noise fuzzy clustering is a useful scheme for analyzing intrinsic data structures through robust estimation of fuzzy c-partition. In this paper, a novel noise rejection scheme for improving fuzzy co-clustering is proposed, which is useful in such cooccurrence information analysis as document classification, where multi-topic co-cluster structure extraction by probabilistic latent semantic analysis (pLSA) is achieved through rejection of the influences of noise objects. Supported by the uniform noise distribution concept in noise fuzzy clustering, a noise cluster having uniform item occurrence probabilities is newly introduced into the pLSA-induced fuzzy co-clustering model. Several numerical experiments demonstrate the advantage of tuning the noise sensitivity of the pLSA-induced objective function.

In this paper an optimization problem to minimize the energy drawn from the network grid by utilizing the harvested energy and dynamic sleeping of the Small Base Stations (SBSs) is presented. Due to the complexity of the optimization problem, a new UEs' movement prediction method is introduced to provide future information for the model to apply an accurate optimization problem. This method is based on a combined approach of Non-linear Autoregressive with External input (NARX) and probabilistic Latent Semantic Analysis (pLSA) to provide accurate prediction for multiple steps. Furthermore, extensive simulation results are presented to show the effectiveness of our approaches in comparison to the optimal results.

The Probabilistic Latent Semantic Analysis has been related with the Singular Value Decomposition. Several problems occur when this comparative is done. Data class restrictions and the existence of several local optima mask the relation, being a formal analogy without any real significance. Moreover, the computational difficulty in terms of time and memory limits the technique applicability. In this work, we use the Nonnegative Matrix Factorization with the Kullback–Leibler divergence to prove, when the number of model components is enough and a limit condition is reached, that the Singular Value Decomposition and the Probabilistic Latent Semantic Analysis empirical distributions are arbitrary close. Under such conditions, the Nonnegative Matrix Factorization and the Probabilistic Latent Semantic Analysis equality is obtained. With this result, the Singular Value Decomposition of every nonnegative entries matrix converges to the general case Probabilistic Latent Semantic Analysis results and constitutes the unique probabilistic image. Moreover, a faster algorithm for the Probabilistic Latent Semantic Analysis is provided.

In practice, to build a machine learning model of big data, one needs to tune model parameters. The process of parameter tuning involves extremely time-consuming and computationally expensive grid search. However, the theory of statistical physics provides techniques allowing us to optimize this process. The paper shows that a function of the output of topic modeling demonstrates self-similar behavior under variation of the number of clusters. Such behavior allows using a renormalization technique. A combination of renormalization procedure with the Renyi entropy approach allows for quick searching of the optimal number of topics. In this paper, the renormalization procedure is developed for the probabilistic Latent Semantic Analysis (pLSA), and the Latent Dirichlet Allocation model with variational Expectation&ndash, Maximization algorithm (VLDA) and the Latent Dirichlet Allocation model with granulated Gibbs sampling procedure (GLDA). The experiments were conducted on two test datasets with a known number of topics in two different languages and on one unlabeled test dataset with an unknown number of topics. The paper shows that the renormalization procedure allows for finding an approximation of the optimal number of topics at least 30 times faster than the grid search without significant loss of quality.

Dynamic and uncertainty are two main features of the industrial process data which should be paid attention when carrying out process data modeling and analytics. In this paper, the dynamical and uncertain data characteristics are both taken into consideration for the regression modeling purpose. Based on the probabilistic latent variable modeling framework, the linear dynamic system is introduced for incorporation of the dynamical data feature. The expectation–maximization Algorithm is introduced for parameter learning of the dynamical probabilistic latent variable model, based on which a new soft sensing scheme is then formulated for online prediction of key/quality variables in the process. An industrial case study illustrates the necessity and effectiveness of introducing the dynamical data information into the probabilistic latent variable model.

Word relatedness computation is an important supporting technology for many tasks in natural language processing. Traditionally, there have been two distinct strategies for word relatedness measurement: one utilizes corpus-based models, whereas the other leverages external lexical resources. However, either solution has its strengths and weaknesses. In this paper, we propose a lexical resource-constrained topic model to integrate the two complementary strategies effectively. Our model is an extension of probabilistic latent semantic analysis, which automatically learns word-level distributed representations forward relatedness measurement. Furthermore, we introduce generalized expectation maximization (GEM) algorithm for statistical estimation. The proposed model not merely inherit the advantage of conventional topic models in dimension reduction, but it also refines parameter estimation by using word pairs that are known to be related. The experimental results in different languages demonstrate the effectiveness of our model in topic extraction and word relatedness measurement.