2022/03/17 by Hugo Schnoering, Schnoering, Hugo
Computer Science · Physics and Astronomy · #Advanced Text Analysis Techniques #Complex Network Analysis Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Web Data Mining and Analysis #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.2203.11152
arxiv created 2022/03/17 · openalex publication_date 2022/03/17 · arxiv updated 2022/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.