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Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

Hamed JelodarYongli WangChi YuanXia FengXia-Hui JiangYanchao LiLiang Zhao

article2017Multimedia tools and applications1,813 citations

Synthesizes over a decade of Latent Dirichlet Allocation research by categorizing major model extensions, cross-disciplinary applications, benchmark datasets, and practical software tools for text mining.

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Modern organizations and researchers face significant challenges in analyzing, structuring, and extracting meaningful patterns from enormous collections of unstructured electronic text. Topic modeling has emerged as a critical automated technique within machine learning to discover hidden themes across vast archives without requiring manual labeling. Latent Dirichlet Allocation (LDA) serves as the foundational, widely adopted framework for these efforts. The article provides a comprehensive survey of LDA-based research from 2003 through 2016, charting the development, core inference mechanisms, domain-specific adaptations, tools, datasets, and open technical challenges across the field.

To conduct this evaluation, the authors performed an extensive literature review of influential academic studies and developments over the 14-year period following the initial formulation of LDA. The survey reviews generative probability principles, parameter estimation and training methods—such as Gibbs sampling, Expectation-Maximization, and Variational Bayes inference—and analyzes empirical implementations spanning text corpora, source code repositories, social networks, and multimedia datasets.

Several key findings emerge from the review. First, Gibbs sampling is the most widely adopted inference method in practice, though variational methods are increasingly used to scale algorithms across distributed frameworks. Second, LDA has evolved far beyond basic text categorization into highly specialized extensions across multiple disciplines. Key application areas include software engineering for bug localization and code refactoring, healthcare and biomedicine for adverse drug reaction discovery, and political science for speech analysis. Third, topic modeling has proven effective in social network analysis, notably on microblogging platforms for hashtag recommendation, user behavior modeling, and real-time detection of sudden events. Finally, the ecosystem is supported by mature open-source tools such as Mallet, Gensim, and Stanford TMT, alongside established multi-domain datasets.

The findings demonstrate that LDA-based frameworks significantly reduce the time, labor, and costs associated with organizing massive textual and relational data. By automatically identifying underlying thematic structures, organizations can improve recommendation systems, enhance risk detection in public health and cybersecurity, and streamline information retrieval across complex digital environments.

For future development, the article highlights seven open research areas that require further work before systems can reach optimal performance. Practitioners and researchers should focus on extending topic models to image classification, audio and music information retrieval, drug safety assessment, user behavior modeling in mobile and social networks, group discovery in graph analytics, and enhanced interactive visualization tools like LDAvis and Termite. While the surveyed models demonstrate high utility across diverse domains, the article notes limitations around handling short texts, high-velocity streaming data, and complex multi-modal inputs, suggesting that implementers carefully evaluate topic quality and scaling trade-offs when deploying these techniques in production.

Nanjing University of Science and Technologyengineeringsoftwaresoftware reengineeringLatent Dirichlet Allocationtopic modelinghierarchical Dirichlet processes
arXiv: 1711.04305
  • Paper: Latent Dirichlet Allocation, David M. Blei et al. (2003). This seminal paper introduces Latent Dirichlet Allocation (LDA), providing the foundational probabilistic model that the survey reviews, categorizes, and evaluates.
  • Paper: Probabilistic Latent Semantic Analysis, Thomas Hofmann (1999). This paper establishes Probabilistic Latent Semantic Analysis (PLSA), the direct generative predecessor to LDA that provides essential conceptual background for statistical topic modeling.
  • Paper: Indexing By Latent Semantic Analysis, Scott Deerwester et al. (1990). This classic paper introduces Latent Semantic Indexing, laying the groundwork for algebraic dimensionality reduction and latent topic discovery in text collections.
  • Paper: Supervised Topic Models, David M. Blei et al. (2007). This work introduces supervised LDA, demonstrating a major structural extension of topic models to incorporate document-level response variables surveyed in the source.
  • Paper: Stochastic variational inference, Matt Hoffman et al. (2012). This article develops stochastic variational inference, a crucial computational advance that enables LDA and related Bayesian topic models to scale to massive document corpora.
  • Paper: Exploring the Space of Topic Coherence Measures, Michael Röder et al. (2015). This paper provides a systematic evaluation of automated topic coherence measures, which are essential evaluation metrics discussed in the survey's intellectual landscape of topic modeling.
  • Paper: Optimizing Semantic Coherence in Topic Models, David Mimno et al. (2011). This work introduces intrinsic semantic coherence metrics for topic models, addressing key interpretability and evaluation challenges synthesized in the survey.
  • Paper: Reading Tea Leaves: How Humans Interpret Topic Models, Jonathan D. Chang et al. (2009). This study establishes human interpretability benchmarks for topic models, detailing critical evaluation methodologies and pitfalls reviewed in the survey.
  • Paper: From Frequency to Meaning: Vector Space Models of Semantics, Peter D. Turney et al. (2010). This comprehensive survey of vector space models provides the broader mathematical and semantic foundation underlying bag-of-words document representations and matrix factorization techniques.
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Abstract

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data, text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modeling, which Latent Dirichlet allocation (LDA) is one of the most popular methods in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper can be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated scholarly articles highly (between 2003 to 2016) related to Topic Modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. Also, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.

Topics

engineeringsoftwaresoftware reengineeringtext documentlatent factorlatent variabletopic modelsocial sciencesassociation rulesmodeling and analysis

Keywords

Latent Dirichlet Allocationtopic modelinghierarchical Dirichlet processestopic distributionsmulti-modal event topic modelprobabilistic aspect mining modelDirichlet class language model

Table of Contents

  • 1. Introduction
  • 2.2.2. A brief look at past work: Research between 2010 to 2011
  • 2.2.3. A brief look at past work: Research between 2012 to 2013
  • 2.2.4. A brief look from some impressive past works: Research in 2016
  • 2.2 Topic Modeling for which the area is used?
  • A. Topic modeling in Linguistic science
  • B. Topic modeling in political science
  • F. Topic modeling in Social Network / Microblogs (such as Twitter)
  • 4. Open source library and tools / datasets / Software packages and tools for the analysis
  • 4.1 library/tools
  • Reference

Knowls

  1. Knowl 1 — Latent Dirichlet Allocation Generative Model and Marginal Likelihood

    model/method

    Latent Dirichlet Allocation (LDA) is an unsupervised generative probabilistic model for collections of discrete data such as text corpora. In LDA, each document is represented as a finite mixture over an underlying set of latent topics, and each topic is characterized by a discrete probability distribution over the vocabulary. Given a corpus DD consisting of MM documents, where document d∈{1,…,M}d \in \{1, \dots, M\} contains NdN_d words, the generative process is defined as follows:

    1. For each topic t∈{1,…,T}t \in \{1, \dots, T\}, draw a multinomial word distribution ϕt∼Dirichlet(β)\phi_t \sim \text{Dirichlet}(\beta).
    2. For each document d∈{1,…,M}d \in \{1, \dots, M\}, draw a multinomial topic mixture distribution θd∼Dirichlet(α)\theta_d \sim \text{Dirichlet}(\alpha).
    3. For each word position n∈{1,…,Nd}n \in \{1, \dots, N_d\} in document dd: a. Draw a specific topic assignment zd,n∼Multinomial(θd)z_{d,n} \sim \text{Multinomial}(\theta_d). b. Draw an observed word wd,n∼Multinomial(ϕzd,n)w_{d,n} \sim \text{Multinomial}(\phi_{z_{d,n}}).

    The marginal probability of the observed corpus DD given the Dirichlet prior hyperparameters α\alpha and β\beta is obtained by integrating over the latent document-topic proportions θd\theta_d and topic-word distributions Φ={ϕ1,…,ϕT}\Phi = \{\phi_1, \dots, \phi_T\}, and summing over all possible latent topic assignments zd,nz_{d,n}:

    p(D∣α,β)=∏d=1M∫p(θd∣α)(∏n=1Nd∑zd,n=1Tp(zd,n∣θd)p(wd,n∣zd,n,Φ))dθdp(D \mid \alpha, \beta) = \prod_{d=1}^M \int p(\theta_d \mid \alpha) \left( \prod_{n=1}^{N_d} \sum_{z_{d,n}=1}^T p(z_{d,n} \mid \theta_d) p(w_{d,n} \mid z_{d,n}, \Phi) \right) d\theta_d

  2. Knowl 2 — Parameter Estimation and Inference Approaches for LDA

    model/method

    Parameter estimation and posterior inference in Latent Dirichlet Allocation involve estimating the latent distributions θ\theta, ϕ\phi, and latent topic assignments zz, as well as optimizing hyperparameters α\alpha and β\beta. Three primary algorithmic paradigms are used:

    • Gibbs Sampling: A Markov Chain Monte Carlo (MCMC) algorithm that generates samples from the joint distribution by iteratively drawing each latent variable conditioned on the current values of all other variables. Collapsed Gibbs sampling integrates out the multinomial parameters θ\theta and ϕ\phi, sampling only the discrete topic indicator variables zz.
    • Expectation-Maximization (EM): An optimization framework for discovering maximum likelihood or maximum a posteriori estimates in the presence of latent variables. It alternates between an Expectation step (E-step), which calculates the expected value of the latent topic states, and a Maximization step (M-step), which optimizes parameter estimates given those expectations.
    • Variational Bayes (VB) Inference: A deterministic approximation method that posits a tractable parametric family of distributions over latent variables and optimizes the variational parameters to minimize the Kullback-Leibler (KL) divergence to the true intractable posterior distribution.

  3. Knowl 3 — Word Velocity and Acceleration for Real-Time Bursty Topic Detection

    equation

    In streaming social text analysis, bursty topic detection identifies sudden spikes in topic volume by modeling the rate of arrival and acceleration of words or word n-grams over time. The time-decayed velocity v^ΔT(t)\hat{v}_{\Delta T}(t) of a word at timestamp tt over a time window ΔT\Delta T is defined as:

    v^ΔT(t)=∑ti≤tXi⋅exp⁡(ti−tΔT)ΔT\hat{v}_{\Delta T}(t) = \sum_{t_i \le t} X_i \cdot \frac{\exp\left(\frac{t_i - t}{\Delta T}\right)}{\Delta T}

    where XiX_i is the frequency of the word in the ii-th text message, tit_i is the timestamp of the message, and ΔT\Delta T acts as a soft moving window size giving higher weight to recent tokens.

    The word acceleration α^(t)\hat{\alpha}(t), capturing the rate of change between two distinct window scales ΔT1\Delta T_1 and ΔT2\Delta T_2 (where ΔT1≠ΔT2\Delta T_1 \ne \Delta T_2), is given by:

    α^(t)=v^ΔT2(t)−v^ΔT1(t)ΔT1−ΔT2\hat{\alpha}(t) = \frac{\hat{v}_{\Delta T_2}(t) - \hat{v}_{\Delta T_1}(t)}{\Delta T_1 - \Delta T_2}

    Bursty topics exhibit strong positive acceleration (α^(t)≫0 \hat{\alpha}(t) \gg 0), enabling their detection while filtering out general or persistent background topics that have near-zero acceleration.

  4. Knowl 4 — Software Tools and Toolboxes for Topic Modeling

    data/table
    Tool Language Inference Method Source Availability / URL
    Mallet Java Gibbs sampling http://mallet.cs.umass.edu/topics.php
    Stanford TMT Java Gibbs sampling https://nlp.stanford.edu/software/tmt/tmt-0.4/
    Mr.LDA Java Variational Bayes https://github.com/lintool/Mr.LDA
    JGibbLDA Java Gibbs sampling http://jgibblda.sourceforge.net/
    Gensim Python Gibbs sampling / Online VB https://radimrehurek.com/gensim
    TopicXP Java (Eclipse plugin) Gibbs sampling http://www.cs.wm.edu/semeru/TopicXP/
    Matlab Topic Modeling Toolbox Matlab Gibbs sampling http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm
    Yahoo_LDA C++ Gibbs sampling https://github.com/shravanmn/Yahoo_LDA
    lda in R R Gibbs sampling https://cran.r-project.org/web/packages/lda/

    Open-source libraries provide implementations of LDA and related models. While most toolkits (including Mallet, JGibbLDA, Matlab Toolbox, and R packages) rely on MCMC Gibbs sampling for parameter inference, distributed frameworks designed for large document clusters (such as Mr.LDA over MapReduce) utilize Variational Bayesian inference.

  5. Knowl 5 — Benchmark Corpora for Topic Modeling Evaluation

    data/table
    Dataset Language Release Year Domain / Content Description
    Reuters-21578 English 1997 Categorized financial and news articles
    Reuters-Volume I (RCV1) English 2004 Large-scale categorized newswire articles
    UDI-TwitterCrawl-Aug2012 English 2012 Multi-million microblog tweet corpus
    SemEval-2013 English 2013 Twitter messages annotated for sentiment and semantics
    Wiki10 English 2009 Wikipedia documents with user-contributed social tags
    Weibo Dataset Chinese 2013 Microblog post dataset from Sina Weibo
    Bag of Words (UCI) English 2008 Multi-domain texts (PubMed, KOS blog, NYTimes, NIPS, Enron)
    CiteULike English 2011 Academic reference sharing metadata and abstracts
    DBLP Dataset English 2011 Computer science bibliographic metadata
    HowNet Lexicon Chinese 2000–2013 Machine-readable bilingual lexical knowledge base
    Virastyar Persian 2013 Persian electronic lexical corpus and poems
    NIPS Conference Abstracts English 2016 Full text and abstracts from NIPS papers (1987–2015)
    20 Newsgroups English 2008 Usenet messages across 20 distinct discussion groups

    Standard benchmark datasets spanning diverse languages, document lengths, and structural formats are used to evaluate topic model performance in classification, document clustering, and semantic retrieval.

  6. Knowl 6 — LDA Applications in Software Engineering

    model/method

    Latent Dirichlet Allocation has been adapted for source code analysis and software maintenance by treating source files, commit logs, and defect logs as textual documents:

    • Software Categorization and Similarity: Models like LACT extract topic distributions from software repositories (e.g., Apache, SourceForge) to calculate inter-project similarity and categorize software by programming language or domain.
    • Software Traceability Link Recovery: Combining LDA with automated link capture connects requirements documents, architectural models, and source code classes based on topic overlap.
    • Coupling Metrics: Relational Topic Models (RTM) form the basis for Relational Topic based Coupling (RTC), which quantifies structural and conceptual coupling between object-oriented classes using latent topic distributions.
    • Bug Localization: LDA models source code repositories and bug reports to retrieve relevant source entities and locate defect locations, outperforming traditional Latent Semantic Indexing (LSI).
    • Malicious Behavior Identification: Topic modeling coupled with Genetic Algorithms characterizes Android applications by pairing user descriptions with sensitive API data-flow signatures to identify malicious patterns.

  7. Knowl 7 — LDA Applications in Biomedical Informatics and Drug Safety

    model/method

    In medical and biological data mining, LDA extensions process literature and clinical records for discovery tasks:

    • Adverse Drug Reaction (ADR) Discovery: Symbolic and probabilistic LDA models extract patterns from pharmacovigilance databases (such as ADReCS and SIDER) to predict unknown drug side effects and support drug repositioning.
    • Pharmacogenomics (PGx): Models like LDA-PKL utilize Kullback-Leibler divergence between topic distributions over biomedical literature (PubMed) to rank and detect gene-drug associations.
    • Functional Genomic Modules: Correspondence Latent Dirichlet Allocation (Corr-LDA) integrates heterogeneous miRNA and mRNA expression datasets to discover functional regulatory modules.
    • Clinical Pathway Extraction and Traditional Medicine: The Symptom-Herb-Diagnosis Topic (SHDT) model applies Author-Topic architectures to clinical records of diabetic patients to link symptoms with herbal prescriptions and disease comorbidities.
    • Healthcare Recommendation: Recommender systems such as iDoctor combine hybrid matrix factorization and LDA to extract physician feature topics from patient review corpora.

  8. Knowl 8 — Geographical and Multimodal Topic Modeling

    model/method

    Geographical topic modeling integrates spatial metadata (GPS coordinates, geotags) and visual data with textual content:

    • Spatial-Textual Integration: GeoFolk uses multi-modal Bayesian models to combine spatial coordinates and textual tags from social media (such as Flickr), enabling location-aware content retrieval.
    • Latent Geographical Topic Analysis (LGTA): Merges text-driven topic extraction with geographical clustering algorithms to model spatial distributions of topics.
    • Geographic Lexical Variation: Decomposes text into geographic and topical components to discover regional linguistic variations and infer an author's location from text.
    • Remote Sensing and Image Clustering: Multiscale LDA models map high-resolution satellite images into visual words and latent topics to perform semantic object-oriented clustering and land-cover segmentation.

  9. Knowl 9 — Topic Modeling in Political Science and Social Networks

    model/method

    In political science and microblog analytics, LDA extensions model opinions, event dynamics, and user preferences:

    • Political Speech and Stance Modeling: Cross-Perspective Topic (CPT) models and two-layer matrix factorization analyze plenary speeches (e.g., European Parliament and U.S. Senate) to detect ideological stances, political agendas, and contrastive opinions across parties.
    • Hashtag and Follower Recommendation: Models such as Hashtag-LDA, TWILITE, and TOT-MMM (Topic-Over-Time Mixed Membership Model) combine temporal clustering and topic distributions to recommend relevant tags and users.
    • Event Tracking and Sentiment Dynamics: Frameworks such as Event and Tweets LDA (ET-LDA) and Hierarchical Dirichlet Processes (HDP) isolate distinct sub-events and track shifts in public sentiment from social media posts.
    • Community and Anomaly Detection: Models like the Author-Topic-Community (ATC) model and Group Latent Anomaly Detection (GLAD) jointly capture user interactions and content to identify latent social circles and behavioral anomalies.

  10. Knowl 10 — Open Research Challenges and Gaps in Topic Modeling

    limitation

    Seven major open challenges and research gaps remain in probabilistic topic modeling:

    1. Joint Image Classification and Annotation: Achieving robust multi-modal integration of visual region descriptors, object geometry, and class labels under extreme viewpoint variations.
    2. Continuous Audio and Music Processing: Formulating continuous distributions (e.g., Gaussian mixtures) over acoustic feature spaces to model singing timbre, instrument mixtures, and audio events.
    3. Drug Safety Mining from Unstructured Social Text: Extracting adverse drug reactions and consumer health expressions from noisy social media to complement formal biomedical databases.
    4. Cross-Cultural Narrative and Demographic Modeling: Bridging multilingual gaps in public comment streams to extract common semantics across languages and demographic groups.
    5. Large-Scale Graph Group Discovery: Scaling Bayesian topic models to discover overlapping communities and anomalous groupings in massive relational networks.
    6. User Behavior and Mobility Modeling: Synergistically combining heterogeneous mobile web logs, location trajectories, and interaction histories into unified topic representations.
    7. Interactive Topic Model Visualization: Designing visual diagnostic tools (such as Termite, LDA-SOM, and LDAvis) that leverage term saliency, distinctiveness, and interactive projections to interpret complex fitted models.

Coverage note — Specific sub-model citations and minor one-off application case studies mentioned in the survey's extensive literature tables were synthesized into the respective domain-level knowls rather than listed individually.

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Citation

MLA
Jelodar, H., et al. “Latent Dirichlet Allocation (LDA) and Topic Modeling: Models, Applications, a Survey”. Multimedia Tools and Applications, vol. 78, no. 11, 2018, pp. 15169–211, https://doi.org/10.1007/s11042-018-6894-4.
APA
Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2018). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169–15211. https://doi.org/10.1007/s11042-018-6894-4
Chicago
Jelodar, H., Y. Wang, C. Yuan, et al. 2018. “Latent Dirichlet Allocation (LDA) and Topic Modeling: Models, Applications, a Survey”. Multimedia Tools and Applications 78 (11): 15169–211. https://doi.org/10.1007/s11042-018-6894-4.
Harvard
Jelodar, H. et al. (2018) “Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey”, Multimedia Tools and Applications, 78(11), pp. 15169–15211. Available at: https://doi.org/10.1007/s11042-018-6894-4.
Vancouver
1. Jelodar H, Wang Y, Yuan C, Feng X, Jiang X, Li Y, Zhao L (2018) Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications 78:15169–15211

BibTeX

@article{Jelodar_2018, title={Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey}, volume={78}, ISSN={1573-7721}, url={http://dx.doi.org/10.1007/s11042-018-6894-4}, DOI={10.1007/s11042-018-6894-4}, number={11}, journal={Multimedia Tools and Applications}, publisher={Springer Science and Business Media LLC}, author={Jelodar, Hamed and Wang, Yongli and Yuan, Chi and Feng, Xia and Jiang, Xiahui and Li, Yanchao and Zhao, Liang}, year={2018}, month=Nov, pages={15169–15211} }
Metadata:Crossref

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