Short Text Topic Modeling: Application to tweets about Bitcoin

TL;DR

This paper explores short text topic modeling on tweets about Bitcoin, comparing three models and demonstrating their application in understanding social media discussions about cryptocurrency.

Contribution

It evaluates three different topic models on short texts and proposes a practical application for the extracted topics, advancing methods for social media text analysis.

Findings

Three topic models evaluated on Bitcoin tweets

Models provide meaningful topic extraction from short texts

Application demonstrates utility in social media analysis

Abstract

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.