# GraphTMT: Unsupervised Graph-based Topic Modeling from Video Transcripts

arXiv: https://arxiv.org/abs/2105.01466 · HTML: https://mlsanity.com/paper/2105.01466/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 11 of 11)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning](https://mlsanity.com/paper/2003.00392/) (2020)
- [Accelerated Hierarchical Density Clustering](https://mlsanity.com/paper/1705.07321/) (2017)
- [Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!](https://mlsanity.com/paper/2004.14914/) (2020)
- [The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress](https://mlsanity.com/paper/2104.07123/) (2021)
- [The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements](https://mlsanity.com/paper/2101.06053/) (2021)
- [An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data](https://mlsanity.com/paper/2012.03468/) (2020)
- [Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling](https://mlsanity.com/paper/1605.00968/) (2016)
- [Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles](https://mlsanity.com/paper/2010.15067/) (2020)
- [Structural Cohesion: Visualization and Heuristics for Fast Computation](https://mlsanity.com/paper/1503.04476/) (2015)

## Built on by (most cited first, 1 of 1)

- [Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person](https://mlsanity.com/paper/2305.14580/) (2023)

## Abstract (arXiv)

To unfold the tremendous amount of multimedia data uploaded daily to social media platforms, effective topic modeling techniques are needed. Existing work tends to apply topic models on written text datasets. In this paper, we propose a topic extractor on video transcripts. Exploiting neural word embeddings through graph-based clustering, we aim to improve usability and semantic coherence. Unlike most topic models, this approach works without knowing the true number of topics, which is important when no such assumption can or should be made. Experimental results on the real-life multimodal dataset MuSe-CaR demonstrates that our approach GraphTMT extracts coherent and meaningful topics and outperforms baseline methods. Furthermore, we successfully demonstrate the applicability of our approach on the popular Citysearch corpus.