Tagged image clustering via topic models

Parallel-fusion LSTM with synchronous semantic and visual information for image captioning

Topic modeling extracts useful potential topics that reflect market information from massive financial news and is widely used in data mining and economic research. Traditional topic modeling approaches such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) lack semantic information, and short texts have feature sparse problems. We develop a topic clustering model based on BERT-LDA joint embedding that takes both contextual semantics and thematic narrative into account. We cluster document embeddings with the HDBSCAN algorithm and utilize a class-based TF-IDF (c-TF-IDF) method to create topic representations. Empirical results show that the BERT-LDA model is competitive compared with traditional and single topic models. It generates coherent topic words that are dissimilar to each other.

A massive of text that is generated every minute is increasing dramatically. Therefore, it is more and more important to find an effective model to automatic classify the amount of text. Topic models is the most powerful techniques in text classification. There are many research results in the field of topic model have been published in scholarly journals. The Latent Dirichlet Allocation (LDA) is one of the most popular topic models in text classification. Researchers have proposed many topic evolution models based on LDA to solve some specific problems in applications of text classification. And some joint models which based on topic models combined other algorithms have been studied to enhance the performance of text classification. In this paper, we investigated three categories topic models for text classification and briefly introduced their advantages and disadvantages in the applications of text mining. Also, we introduce the generated process of documents and illustrate the graphical model for each topic models.

Integrating Big Data Into Evaluation: R Code for Topic Identification and Modeling

Topic modelling is an important technique for extracting meaningful insights from large volumes of unstructured text data. The paper presents a federated technique for topic modelling that is based on a novel approach of the Latent Dirichlet Allocation (LDA) method for topic model generation. The proposed approach enables a topic model to be developed in a distributed environment using data continually generated from multiple sources without the need for sharing actual data. The first iteration of the topic modelling uses unsupervised LDA at each device generating the data. The results of each device are aggregated at a central server to generate a set of seed words that are used for guided LDA by the subsequent iterations of topic modelling. The proposed work, Federated LDA (F-LDA) has been evaluated using two datasets: a text dataset of dialogues between patients and doctors based on factual conversations and another comprising tweets related to depression. Comparing the performance of F-LDA with that of a centralized LDA, it was observed that F-LDA results in improved coherence score as well as diversity score in comparison to centralized LDA. This indicates that F-LDA achieves better interpretable topics covering a wide range of themes without redundancy.

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.

Analysis of Consumer buying patterns is crucial for vendors to understand the necessities and favorites of their audience. With the increasing availability of digital data, collecting large amounts of consumer feedback and opinions has become easier. However, analyzing such data is challenging due to the presence of unstructured text data. Latent Dirichlet Allocation (LDA) is a prevalent topic modeling procedure that can be used to analyze unstructured text data. LDA allows for the identification of latent topics that are present in the data, enabling businesses to gain insights into consumer behavior. By applying LDA to consumer feedback data, businesses can identify the key topics that are most important to their customers. Additionally, LDA can be used to track changes in consumer preferences over time, allowing businesses to stay up-to-date with evolving market trends. Overall, LDA is a powerful tool for analyzing consumer behavior and can provide valuable insights for businesses looking to improve their products and services. In this work, LDA is employed to analyze consumer tweets using topic modeling. LDA has successfully generated topic and

In an era dominated by an unprecedented deluge of textual information, the need for effective methods to make sense of large datasets is more pressing than ever. This article takes a pragmatic approach to unraveling the intricacies of topic modeling, with a specific focus on the widely used Latent Dirichlet Allocation (LDA) algorithm. The initial segment of the article lays the groundwork by exploring the practical relevance of topic modeling in real-world scenarios. It addresses the everyday challenges faced by researchers and professionals dealing with vast amounts of unstructured text, emphasizing the potential of topic modeling to distill meaningful insights from seemingly chaotic data. Moving beyond theoretical abstraction, the article then delves into the mechanics of Latent Dirichlet Allocation. Developed in 2003 by Blei, Ng, and Jordan, LDA provides a probabilistic framework to identify latent topics within documents. The article takes a step-by-step approach to demystify LDA, offering a practical understanding of its components and the Bayesian principles governing its operation. A significant portion of the article is dedicated to the practical implementation of LDA. It provides insights into preprocessing steps, parameter tuning, and model evaluation, offering readers a hands-on guide to applying LDA in their own projects. Real-world examples and case studies showcase how LDA can be a valuable tool for tasks such as document clustering, topic summarization, and sentiment analysis. However, the journey through LDA is not without challenges, and the article candidly addresses these hurdles. Topics such as determining the optimal number of topics, the sensitivity of results to parameter settings, and the interpretability of outcomes are discussed. This realistic appraisal adds depth to the article, helping readers navigate the nuances and potential pitfalls of employing LDA in practice. Beyond the technical intricacies, the article explores the broad spectrum of applications where LDA has proven its efficacy. From text mining and information retrieval to social network analysis and healthcare informatics, LDA has left an indelible mark on diverse domains. Through practical examples, the article illustrates how LDA can be adapted to different contexts, showcasing its versatility as a tool for uncovering latent patterns. Keywords: Topic Modeling, Latent Dirichlet Allocation, Text Mining, Natural Language Processing, Document Clustering, Bayesian Inference.

With the quick penetration of Internet applications, online media have become an important carrier of public opinions. The opinions and comments expressed by young college students—one of the most active netizen groups—on the Internet have turned out to be an essential part of the online public opinions in colleges and universities. However, the existing systems generally employ simple statistical methods to analyze the effect of online public opinions on the image and reputation development of colleges and universities without taking account of other factors, such as the hotness characteristics of online public opinions and semantic information. Therefore, on the basis of Public Opinion Hotness Index and time series-based trend analysis, as well as the topics extracted using the latent Dirichlet allocation (LDA) topic model, this study aims to improve the analysis performance on the online public opinions in colleges and universities using short-term trend prediction results. The experience and lessons learned from a real case may provide strong data support and feasible suggestions for colleges and universities in analyzing and guiding the online public opinions.

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

This paper presents a multiple feature fusion method using topic model for social image visualization. Images in social media are represented from several aspects such as their visual information and tags. The proposed method extracts low-level features from social images and their tags and calculates their integrated high-level features. Specifically, the proposed method applies multilayer multimodal probabilistic Latent Semantic Analysis (mm-pLSA) to the low-level visual and tag features to obtain the high-level features. Then, by applying dimensionality reduction techniques to the obtained features, successful visualization becomes feasible.

Given that unstructured data is increasing exponentially everyday, extracting and understanding the information, themes, and relationships from large collections of documents is increasingly important to researchers in many disciplines including biomedicine. Latent Dirichlet Allocation (LDA) is an unsupervised topic modeling technique based on the "bag-of-words" assumption that has been applied extensively to unveil hidden semantic themes within large sets of textual documents. Recently, it was extended using the "bag-of-n-grams" paradigm to account for word order. In this paper, we present an alternative phrase based LDA model to move from a bag of words or n-grams paradigm to a "bag-of-key-phrases" setting by applying a key phrase extraction technique, the C-value method, to further explore latent themes. We evaluate our approach by using a phrase intrusion user study and demonstrate that our model can help LDA generate better and more interpretable topics than those generated using the bag-of-n-grams approach. Given topic models essentially are statistical tools, an important problem in topic modeling is that of visualizing and interacting with the models to understand and extract new information from a collection. To evaluate our phrase based modeling approach in this context, we incorporate it in an open source interactive topic browser. Qualitative evaluations of this browser with biomedical experts demonstrate that our approach can aid biomedical researchers gain better and faster understanding of their document collections.

Topic models have recently shown to be really useful tools for the analysis of microarray experiments. In particular they have been successfully applied to gene clustering and, very recently, also to samples classification. In this latter case, nevertheless, the basic assumption of functional independence between genes is limiting, since many other a priori information about genes’ interactions may be available (co-regulation, spatial proximity or other a priori knowledge). In this paper a novel topic model is proposed, which enriches and extends the Latent Dirichlet Allocation (LDA) model by integrating such dependencies, encoded in a categorization of genes. The proposed topic model is used to derive a highly informative and discriminant representation for microarray experiments. Its usefulness, in comparison with standard topic models, has been demonstrated in two different classification tests.KeywordsTopic ModelLatent Dirichlet AllocationMultinomial DistributionProbabilistic Latent Semantic AnalysisLatent Dirichlet Allocation ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Since Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) were introduced, many revised or extended topic models have appeared. Due to the intractable likelihood of these models, training any topic model requires to use some approximation algorithm such as variational approximation, Laplace approximation, or Markov chain Monte Carlo (MCMC). Although these approximation algorithms perform well, training a topic model is still computationally expensive given the large amount of data it requires. In this paper, we propose a new method, called non-simultaneous sampling deactivation, for efficient approximation of parameters in a topic model. While each random variable is normally sampled or obtained by a single predefined burn-in period in the traditional approximation algorithms, our new method is based on the observation that the random variable nodes in one topic model have all different periods of convergence. During the iterative approximation process, the proposed method allows each random variable node to be terminated or deactivated when it is converged. Therefore, compared to the traditional approximation ways in which usually every node is deactivated concurrently, the proposed method achieves the inference efficiency in terms of time and memory. We do not propose a new approximation algorithm, but a new process applicable to the existing approximation algorithms. Through experiments, we show the time and memory efficiency of the method, and discuss about the tradeoff between the efficiency of the approximation process and the parameter consistency.

Latent Dirichlet allocation (LDA) is a statistical model that is often used to discover topics or themes in a large collection of documents. In the LDA model, topics are modeled as discrete distributions over a finite vocabulary of words. The LDA is also a popular choice to model other datasets spanning a discrete domain, such as population genetics and social networks. However, in order to model data spanning a continuous domain with the LDA, discrete approximations of the data need to be made. These discrete approximations to continuous data can lead to loss of information and may not represent the true structure of the underlying data. We present an augmented version of the LDA topic model, where topics are represented using Gaussian mixture models (GMMs), which are multi-modal distributions spanning a continuous domain. This augmentation of the LDA topic model with Gaussian mixture topics is denoted by the GMM-LDA model. We use Gibbs sampling to infer model parameters. We demonstrate the utility of the GMM-LDA model by applying it to the problem of clustering sleep states in electroencephalography (EEG) data. Results are presented demonstrating superior clustering performance with our GMM-LDA algorithm compared to the standard LDA and other clustering algorithms.