论文标题
短文主题建模：应用于比特币的推文
Short Text Topic Modeling: Application to tweets about Bitcoin
论文作者
论文摘要
了解文本集合的语义是一项艰巨的任务。主题模型是概率模型，旨在从文档语料库中提取“主题”。当语料库由简短的文本（例如社交网络上的帖子）组成时，此任务特别困难。在以前的几篇研究论文之后，我们在本文中探讨了一系列有关比特币的推文。在这项工作中，我们训练三个主题模型，并通过多个分数评估他们的产出。我们还提出了提取主题的具体应用。
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.
