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Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey
Hamed Jelodar, Yongli Wang, Chi Yuan, Xia Feng, Xiahui Jiang, Yanchao Li, Liang Zhao
TL;DR
Topic modeling supports semantic mining and latent discovery, but LDA-based approaches span diverse models, subjects, tools, datasets, and applications. This paper surveys those approaches, identifies seven insufficiently solved issues, and positions the review as a resource for researchers and future work.
Problem
The paper addresses the need for an overview of topic-modeling approaches based on LDA across models, tools, datasets, applications, and subjects.
Method
The paper investigates LDA-based topic-modeling approaches across different subjects and summarizes relevant methods, tools, datasets, and applications.
Results
The review identifies seven important issues in topic modeling that remain insufficiently solved and presents them as directions for future research.
Takeaways & Limitations
The paper is intended as a significant source for researchers and future work on text mining with LDA-based topic modeling.
Takeaways & Limitations
The review reports that several topic-modeling issues remain insufficiently solved, leaving gaps for future research.
Abstract
from arXiv · showhide
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.
1 Introduction
Topic modeling based on LDA is applied across diverse text-mining domains, including political science, software engineering, social media, and opinion analysis. This paper surveys LDA-based models, applications, challenges, tools, and datasets using scholarly articles from 2003 to 2016.
- LDA-based topic modeling supports topic discovery and semantic mining across natural language processing, text mining, social media analysis, and information retrieval.
- Political-science studies use topic models to identify event-specific and long-running topics, analyze political texts, and examine relationships among posts, commentators, and responses.
- In software engineering, LDA has been used to extract topics from source code and visualize software similarity across large software-project collections.
- Applications span source-code analysis, opinion and aspect mining, event detection, image classification, recommendation systems, and emotion classification.
- Prior surveys examined topic modeling in software engineering, opinion mining, and sentiment analysis, while this paper emphasizes LDA-based models together with applications, tools, and datasets.
- The paper investigates articles from 2003 to 2016 to identify research development, current trends, and the intellectual structure of LDA-based topic modeling, while summarizing challenges and introducing notable tools and datasets.
2 Computer science and topic modeling
Topic modeling represents documents through latent topics and word distributions, with LDA providing a widely used probabilistic foundation. The section describes LDA’s generative process, inference methods, and extensions for temporal, relational, multimodal, and short-text applications.
- Topic modeling foundations: Topic models represent documents as mixtures of latent topics, with each topic characterized by a distribution over words.They approximate documents as combinations of words drawn from probable topic-specific baskets rather than interpreting word meaning directly.
- LDA foundations: LDA is an unsupervised generative model in which document-topic and topic-word distributions share Dirichlet priors.Its latent variables include document-level topic proportions and topic assignments, while observed variables are the corpus words.
- LDA foundations: LDA generates each word by selecting a topic from a document’s topic distribution and then selecting the word from that topic’s word distribution.The generative process samples topic distributions for documents and word distributions for topics before assigning topics and words.
- Inference methods: Variational inference, expectation propagation, Gibbs sampling, expectation-maximization, and variational Bayes are described as approaches for estimating LDA parameters.Gibbs sampling uses conditional distributions in a Monte Carlo Markov-chain procedure, while EM alternates expectation and maximization steps.
- LDA extensions: LDA extensions adapt topic modeling to evolving corpora, document networks, short texts, tags, and multimodal social events.DTM models topic evolution over sequential documents; RTM links document content with network structure; BTM models global word co-occurrences in short texts; rLDA uses other documents and tags; mmETM tracks multimodal event evolution.
- LDA extensions: ET-LDA jointly performs event segmentation and topic modeling on Twitter data using Gibbs sampling to estimate topic distributions.The framework is designed to extract general topics from an entire tweet collection while analyzing tweeting behavior.
3 Topic Modeling for which the area is used?
LDA-based topic modeling has been applied across linguistic, political, biomedical, clinical, and geographical research. These applications adapt topic models to discover patterns in language, political attention, medical processes, and location-linked content.
- LDA-based topic modeling spans linguistic, political, medical, biomedical, geographical, and location-focused research.
- Linguistic science: In linguistic research, topic models support machine translation, topic visualization, supervised modeling, and analysis of geographic-temporal social-network data.One reviewed translation approach dynamically biases statistical machine translation toward relevant translations.
- Political science: Political applications analyze attention, evolving topics, contrasting opinions, political behavior, emotional reactions, and political standpoints.The Cross-Perspective Topic model identifies opinions from multiple views and their differences for a given topic.
- Medical and biomedical: Biomedical and clinical applications use LDA-based models to identify biological terminology, rank gene-drug relations, and discover hidden treatment patterns.Clinical-process models estimate latent patterns from care-flow logs for classification and discovery of similar medical activities.
- Geographical and locations: Geographical topic modeling combines textual and spatial information to discover geographic topics and analyze location-linked social-media content.GeoFolk merges text features with spatial knowledge and is described as an extension of LDA.
4 Open source library and tools / datasets / Software packages and tools for the analysis
The paper introduces topic-modeling tools and datasets intended to support organization, search, understanding, testing, and evaluation of large text collections. It highlights the availability of commercial, amateur, professional, and open-source software alongside datasets varying in subject, size, dimension, and language.
- Topic-modeling tools help organize, search, and understand vast amounts of information.
- Tools: Available software includes professional, amateur, commercial, and open-source tools for topic modeling and analysis.MALLET is identified as a highly scalable topic-model package.
- Datasets: Public datasets differ in subject, size, dimension, and language and provide standard sources for testing and evaluation.The paper examines well-known datasets and lists popular datasets in various languages.
5 Discussion and seven important issues in challenges
The review identifies unresolved challenges and future-work directions in topic modeling. These include image understanding, music analysis, drug safety, political and social behavior analysis, graph mining, user modeling, and visualization of large text collections.
- The review identifies seven important topic-modeling issues as insufficiently solved and presents them as directions for future research.
- Image analysis: Image-focused topic modeling addresses classification, annotation, semantic interpretation, and object detection in large or satellite images.Reviewed approaches treat image segments as words and images as documents, with supervised topic modeling applied to classification.
- Music analysis: Music-information research has comparatively few topic-modeling studies, including modified LDA for continuous audio data and retrieval.One model represents topics as Gaussian distributions over audio-feature data and evaluates 1,214 audio documents.
- Drug discovery: Drug safety and performance remain challenging areas where topic modeling may support mining biological documents and drug-repositioning investigations.A reviewed framework modeled drugs as documents and phenotypes as words, identifying 908 drugs with new capacity indications from SIDER.
- Social and large-scale analysis: Open challenges also concern political narratives, group discovery in large graphs, user behavior, and visualization of textual topical structure.The review links these challenges to social media, criminal-organization analysis, large networks, and big-data text visualization.
6 Conclusion
The paper presents topic modeling as a text-mining approach that represents documents through probabilistic word distributions and latent topics. Its review of LDA-based research from 2003 to 2016 is intended to serve as a source for researchers and future work.
- Topic models represent documents as probabilistic selections of words from latent topic-specific baskets rather than understanding word meanings directly.This representation supports views of collections, individual documents, and relationships among documents.
- The review examines scholarly articles on LDA-based topic modeling across multiple sciences from 2003 to 2016.
- The authors position the paper as a significant source and opportunity for researchers and future work in text mining with LDA-based topic modeling.