Topic Models
209 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
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
Reddit Ideology Database
Latest papers with no code
Uncovering Latent Themes of Messaging on Social Media by Integrating LLMs: A Case Study on Climate Campaigns
Furthermore, this method efficiently maps the text and the newly discovered themes, enhancing our understanding of the thematic nuances in social media messaging.
The Geometric Structure of Topic Models
We introduce and demonstrate the applicability of our approach based on a topic model derived from a corpus of scientific papers taken from 32 top machine learning venues.
Topic Modeling as Multi-Objective Contrastive Optimization
Secondly, we explicitly cast contrastive topic modeling as a gradient-based multi-objective optimization problem, with the goal of achieving a Pareto stationary solution that balances the trade-off between the ELBO and the contrastive objective.
RankSum An unsupervised extractive text summarization based on rank fusion
In this paper, we propose Ranksum, an approach for extractive text summarization of single documents based on the rank fusion of four multi-dimensional sentence features extracted for each sentence: topic information, semantic content, significant keywords, and position.
CFTM: Continuous time fractional topic model
This approach incorporates fractional Brownian motion~(fBm) to effectively identify positive or negative correlations in topic and word distribution over time, revealing long-term dependency or roughness.
Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses
We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin.
Topic Modelling: Going Beyond Token Outputs
The output is commonly a set of topics consisting of isolated tokens that often co-occur in such documents.
Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model
To prevent widespread consequences, platforms are eager to predict these videos' impact on viewers' mental health.
Discovering Significant Topics from Legal Decisions with Selective Inference
We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalised regressions and post-selection significance tests.
Dynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notes
However, few topic models are built for longitudinal settings, and they fail to keep consistent topics and capture temporal trajectories for each document.
