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Sparse dynamic topic model with topic birth and death over time

KNOWLEDGE AND INFORMATION SYSTEMS 2025年第6期67卷 5019-5041页

作者： Zhou, Rui Wang, Feifei Liu, Chang Lu, Xiaoling Renmin Univ China Ctr Appl Stat Beijing Peoples R China Renmin Univ China Sch Stat Beijing Peoples R China Renmin Univ China Innovat Platform Beijing 100872 Peoples R China Agr Dev Bank China Beijing Peoples R China

Over the past twenty years, topic modeling has gradually become popular as a powerful tool, extracting useful and meaningful latent representations from large texts. Research on topic evolution, focusing on the representation of changes in topics over time, has begun to attract extensive attention in the fields of information retrieval and data mining. The dynamic topic model is a classical model for topic evolution. It assumes all topics exist throughout the entire time period, overlooking the fact that topics that were previously important are no longer considered, and new topics can also emerge. To address this issue, we propose a novel Bayesian sparse dynamic topic model, utilizing a spike-and-slab prior distribution to capture topic birth and death. The results demonstrate that our proposed model can effectively estimate both the topic distribution and topic sparsity at the same time. Furthermore, simulations and empirical studies on two real-world datasets demonstrate that our proposed model outperforms the classical dynamic topic model and provides rich semantic information on focused topics.

关键词： topic evolution Sparse topic Bayesian collapsed variational inference dynamic topic model

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Bayesian sparse joint dynamic topic model with flexible lead-lag order

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INFORMATION SCIENCES 2022年 616卷 392-410页

作者： Wang, Feifei Zhou, Rui Feng, Yichao Lu, Xiaoling Renmin Univ China Ctr Appl Stat Beijing Peoples R China Renmin Univ China Sch Stat Beijing Peoples R China JD Hlth Int Inc Beijing Peoples R China

Currently, text documents from multiple sources have become available in many fields. It is of great interest to study the relationship between documents from different sources and uncover the underlying causality. Zhu et al. (2021) proposed a joint dynamic topic model (JDTM). They classified all topics into three groups and used the ``shared topics" with a fixed time lag order to characterize the shared information between two corpora. Although JDTM is a powerful tool for discovering the lead-lag relationship, there are two potential shortcomings. First, different shared topics should have distinct meanings, which should lead to different time lag orders between the two corpora. Second, for dynamic documents, not all topics are represented in each time slice, and thus topic sparsity should be considered. To address these two problems, we propose a sparse joint dynamic topic model (SJDTM) with a flexible lead-lag order. We assume a birth-and-death mechanism for all topics and a flexible lead-lag order for different shared topics. The performance of SJDTM is evaluated using both synthetic data and two real text corpora consisting of conference papers and journal papers. (c) 2022 Elsevier Inc. All rights reserved.

关键词： dynamic topic model Lead-lag relationship topic evolution Sparse modeling

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Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec

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SCIENTOMETRICS 2022年第3期127卷 1543-1563页

作者： Gao, Qiang Huang, Xiao Dong, Ke Liang, Zhentao Wu, Jiang Wuhan Univ Sch Informat Management 299 Bayi Rd Wuhan 430072 Peoples R China Wuhan Univ Ctr Studies Informat Resources 299 Bayi Rd Wuhan 430072 Peoples R China

The combination of the topic model and the semantic method can help to discover the semantic distributions of topics and the changing characteristics of the semantic distributions, further providing a new perspective for the research of topic evolution. This study proposes a solution for quantifying the semantic distributions and the changing characteristics based on words in topic evolution through the dynamic topic model (DTM) and the word2vec model. A dataset in the field of Library and information science (LIS) is utilized in the empirical study, and the topic-semantic probability distribution is derived. The evolving dynamics of the topics are constructed. The characteristics of evolving dynamics are used to explain the semantic distributions of topics in topic evolution. Then, the regularities of evolving dynamics are summarized to explain the changing characteristics of semantic distributions in topic evolution. Results show that no topic is distributed in a single semantic concept, and most topics correspond to various semantic concepts in LIS. The three kinds of topics in LIS are the convergent, diffusive, and stable topics. The discovery of different modes of topic evolution can further prove the development of the field. In addition, findings indicate that the popularity of topics and the characteristics of evolving dynamics of topics are irrelevant.

关键词： topic evolution Semantics topic model word2vec dynamic topic model

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dynamic topic model for Tracking topic Evolution and Measuring Popularity of Scientific Literature 6

Dynamic Topic Model for Tracking Topic Evolution and Measuri...

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6th IEEE International Conference on Data Science in Cyberspace, DSC 2021

作者： Liu, Yezheng Wang, Jicheng Qian, Yang Jiang, Yuanchun Sun, Jianshan Chai, Yidong School Of Management Hefei University Of Technology China

ISBN: (纸本)9781665418157

With the progress of science and technology, a large number of scientific papers are published every year. Faced with such large data, identifying high-value research and hot research directions has become an interesting and important task. Previous studies focus less on the popularity measurement of research papers. In this paper, we use a dynamic topic model (DTM) for tracking the topic evolution trend and measuring the popularity of research papers based on the topic hotness. To test our model effectiveness, we conduct experiments on 5,964 research papers in the category of management. Our results show that DTM can help to monitor topic evolution and measuring popularity for scientific literature. © 2021 IEEE.

关键词： dynamic topic model popularity measurement topic evolution

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Tracking urban geo-topics based on dynamic topic model

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2020年 79卷 101419-000页

作者： Yao, Fang Wang, Yan Univ Florida Dept Urban & Reg Planning Coll Design Construct & Planning 1480 Inner Rd Gainesville FL 32601 USA Univ Florida Dept Urban & Reg Planning POB 115706 Gainesville FL 32611 USA Univ Florida Florida Inst Built Environm Resilience POB 115706 Gainesville FL 32611 USA

Modern cities are facing critical environmental and social problems that are difficult to solve using conventional planning approaches due to the cities' magnitude and complexity. Recent developments in sensing technologies and urban computing, however, integrate new data resources and technologies to tackle these challenges. Popular social networking platforms such as Twitter provide new data sources on important events (e.g., cultural activities, political campaigns, accidents, crises) providing rich knowledge about urban systems and human dynamics. This research is intended to develop a method for effectively monitoring important information during such events and helping with planning and policymaking. We use semantically similar and geographically close geo-topics to represent important local events. This research proposes a data-driven system for detecting and tracking the semantic, spatial, and temporal dynamics of these geo-topics, specifically designed for geotagged tweets. The system consists of data preprocessing, geo-topic generation, and geo-topic tracking modules. The preprocessing module can remove robotic and semantically trivial texts. In the geo-topic generation module, we use spatial factors to measure the spatial impacts of geo-tagged tweets by applying an exponential decay function to the pairwise distances between tweets. We then improve the dynamic topic model (DTM) by embedding the spatial factors to enable the generation of geo-topics in semantic, spatial, and temporal dimensions simultaneously. The geo-topic tracking module monitors semantic change by detecting changes in certain keywords' probabilities and the volumes of tweets belonging to different geo-topics. This module also uses radius of gyration and trajectory-pattern mining to track and analyze the movement patterns of geo-topics. We employed the tracking system in three disaster cases in different U.S. cities to track small-scale emergencies and crises. These implementations demon

关键词： dynamic topic model Emergency management Geo-topic tracking Social networking platforms Urban computing

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Forecasting Financial Market Volatility Using a dynamic topic model

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ASIA-PACIFIC FINANCIAL MARKETS 2017年第3期24卷 149-167页

作者： Morimoto, Takayuki Kawasaki, Yoshinori Kwansei Gakuin Univ Dept Math Sci 2-1 Gakuen Sanda Hyogo 6691337 Japan Inst Stat Math Dept Stat Modeling 10-3 Midori Cho Tachikawa Tokyo 1908562 Japan SOKENDAI 10-3 Midori Cho Tachikawa Tokyo 1908562 Japan

This study employs big data and text data mining techniques to forecast financial market volatility. We incorporate financial information from online news sources into time series volatility models. We categorize a topic for each news article using time stamps and analyze the chronological evolution of the topic in the set of articles using a dynamic topic model. After calculating a topic score, we develop time series models that incorporate the score to estimate and forecast realized volatility. The results of our empirical analysis suggest that the proposed models can contribute to improving forecasting accuracy.

关键词： Big data Online news dynamic topic model topic score Forecasting Realized volatility

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Method for Extraction of Purchase Behavior and Product Character Using dynamic topic model 16

Method for Extraction of Purchase Behavior and Product Chara...

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16th IEEE International Conference on Data Mining (ICDM)

作者： Emoto, Mamoru Univ Tokyo Sch Engn Tokyo 1138654 Japan

ISBN: (纸本)9781509059102

In this study, we focus on extraction of latent topic transition from POS data. POS analysis is conducted to obtain the frequent pattern of customer ' s behavior. The fundamental method for POS analysis is to conduct market basket analysis. By doing Market basket analysis, the sets of products that are often bought at the same time can be extracted. In market basket analysis, however, the effect of time series is not considered. We conducted the experiment based on two hypotheses. One is that each product has several topics. The other is that the proportion of each product on a topic changes as the period changes. To extract topics and their changes, we use dynamic topic model (DTM), which is an extended model of Latent Dirichlet Allocation (LDA). Then we obtain the change of the topic-word distribution on each topic. Different topic has different characters, but it seems that there is a relationship between each topic. Therefore, we conduct correlation analysis to several items. From the result of visualization of product features vector of several items, we can obtain that each product has unique time-series change of product feature. This study is also conducted to reveal product features vector based on Customer Purchase Behavior. By using DTM, each basket is transformed into probability distribution vector, and we use this topic vectors as each basket's evaluation result of topic features. We divide POS data into 12 groups by purchasing time and create a heat map that indicates changes of topic proportion as time advances. By conducting this analysis, we can grasp customer behavior based on the topic vector space. These analysises reveals product features are created based on topic correlations and its change and customer behavior can be extracted as the change of topic proportion, so the results show that the presented method is promising in the extraction of products' features and customer behavior.

关键词： dynamic topic model Latent Dirichlet Allocation NLP POS data

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dynamic Dual Sparse topic model: Integrating Temporal dynamics and Sparsity with Spike and Slab Priors into topic model 16

Dynamic Dual Sparse Topic Model: Integrating Temporal Dynami...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Masuda, Tatsuki Nakagawa, Kei Hoshino, Takahiro Graduate School of Economics Keio University Riken Center for Aip Tokyo Japan Nomura Asset Management Co. Ltd. Osaka Metropolitan University Innovation Lab Tokyo Japan Riken Center for Aip Keio University Faculty of Economics Tokyo Japan

ISBN: (纸本)9798350377903

topic modeling is a statistical technique that identifies underlying abstract 'topics' in a corpus of texts. Notable among current topic modeling methodologies are the dynamic topic model (DTM), which captures the temporal dynamics of topics, and the dynamic Sparse model (DSM), which emphasizes the sparsity in the distributions of topics and words. However, no existing model appropriately integrates both the temporal progression and the sparsity within these distributions. Therefore, in this study, we propose the dynamic Dual Sparse topic model (DDSTM), which employs the Spike and Slab Prior as its prior distribution, thereby extending the capabilities of both DTM and DSM. The DDSTM is distinct in its ability to simultaneously manage the temporal dynamics and sparsity in topic distributions, significantly enhancing the model's interpretability. Theoretically, we prove that the DDSTM maintains the same level of sparsity as the existing DSM models. In the empirical analysis, we use real-world text data to examine the characteristics of the DDSTM compared to existing topic models. © 2024 IEEE.

关键词： dynamic topic model sparse topic model spike and slab prior topic model

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dynamic topic modelling for exploring the scientific literature on coronavirus: an unsupervised labelling technique

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2024年 1-31页

作者： Guillen-Pacho, Ibai Badenes-Olmedo, Carlos Corcho, Oscar Univ Politecn Madrid Ontol Engn Grp Madrid Spain Univ Politecn Madrid Comp Sci Dept Madrid Spain

The work presented in this article focusses on improving the interpretability of probabilistic topic models created from a large collection of scientific documents that evolve over time. Several time-dependent approaches based on topic models were compared to analyse the annual evolution of latent concepts in the CORD-19 corpus: dynamic topic model, dynamic Embedded topic model, and BERtopic. Then COVID-19 period (December 2019-present) has been analysed in greater depth, month by month, to explore the evolution of what is written about the disease. The evaluations suggest that the dynamic topic model is the best choice to analyse the CORD-19 corpus. A novel topic labelling strategy is proposed for dynamic topic models to analyse the evolution of latent concepts. It incorporates content changes in both the annual evolution of the corpus and the monthly evolution of the COVID-19 disease. The generated labels are manually validated using two approaches: through the most relevant documents on the topic and through the documents that share the most semantically similar label topics. The labelling enables the interpretation of topics. The novel method for dynamic topic labelling fits the content of each topic and supports the semantics of the topics.

关键词： dynamic topic model topic labelling topic interpretability CORD-19

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Image sequence analysis using dynamic topic models 33

Image sequence analysis using Dynamic Topic Models

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

作者： Bhatia, Amit Bomberger, Neil BAE Syst 4721 Emperor Blvd Durham NC 27703 USA BAE Syst 11091 Sunset Hills Rd Reston VA 20190 USA

ISBN: (纸本)9781510674332;9781510674325

A topic model is a probabilistic method for data analysis and characterization that provides insight into the topics that comprise each document in a corpus, where each topic is described by an associated word distribution. A dynamic topic model is an extension of this model that can be applied to time series data. These models have typically been applied to the text domain where the concepts of tokens and words are well defined. Applying these models to the image domain is non-obvious because the concepts of tokens and words need to hand-crafted. In this work, we apply the dynamic topic model to a sequence of images to provide insight into their dynamic nature, e.g., by helping to identify interesting locations in time that correspond to change in operating conditions We apply this model to images from the KITTI dataset and show that the model captures the evolving nature of these topics over time.

关键词： Image sequence topic model dynamic topic model

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