Topic Models
210 papers with code • 6 benchmarks • 12 datasets
A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for the discovery of hidden semantic structures in a text body.
Benchmarks
These leaderboards are used to track progress in Topic Models
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Libraries
Use these libraries to find Topic Models models and implementationsDatasets
AG News
Reddit
New York Times Annotated Corpus
Arxiv HEP-TH citation graph
OpoSum
Latest papers with no code
Contrastive News and Social Media Linking using BERT for Articles and Tweets across Dual Platforms
Inspired by the success of the CLIP model in computer vision, which employs contrastive learning to model similarities between images and captions, this paper introduces a contrastive learning approach for training a representation space where linked articles and tweets exhibit proximity.
Labeled Interactive Topic Models
To facilitate user interaction with these neural topic models, we have developed an interactive interface.
Profiling Irony & Stereotype: Exploring Sentiment, Topic, and Lexical Features
Social media has become a very popular source of information.
Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling
Besides, we provide a simple solution extending a neural topic model to reduce the effect of noisy out-of-topics text generation from PLMs.
Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation
In addressing the imbalanced issue of data within the realm of Natural Language Processing, text data augmentation methods have emerged as pivotal solutions.
TopicAdapt- An Inter-Corpora Topics Adaptation Approach
Topic models are popular statistical tools for detecting latent semantic topics in a text corpus.
Evaluating Dynamic Topic Models
There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs).
Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews
As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers.
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections
Experts across diverse disciplines are often interested in making sense of large text collections.
Two to Five Truths in Non-Negative Matrix Factorization
In this paper, we explore the role of matrix scaling on a matrix of counts when building a topic model using non-negative matrix factorization.
