Short Text Topic Modeling: Application to tweets about Bitcoin
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Abstract (arXiv)
Understanding the semantic of a collection of texts is a challenging task. Topic models are probabilistic models that aims at extracting "topics" from a corpus of documents. This task is particularly difficult when the corpus is composed of short texts, such as posts on social networks. Following several previous research papers, we explore in this paper a set of collected tweets about bitcoin. In this work, we train three topic models and evaluate their output with several scores. We also propose a concrete application of the extracted topics.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 23 references that are in the map. First posted on arXiv 17 Mar 2022.
Builds on
3 of 23 references.
References · 3
- 2017Autoencoding Variational Inference For Topic Modelscited by 168
- 2013Probabilistic Latent Semantic Analysiscited by 63
- 2019Short Text Topic Modeling Techniques, Applications, and Performance: A Surveycited by 38
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Citing papers per year: 2023: 1.
1 paper.