Exploring Topic Models on Short Texts: A Case Study with Crisis Data

S. Manna, Oras Phongpanangam
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引用次数: 8

Abstract

In recent years, social media platforms like Twitter and Facebook have become one of the crucial sources of information for a wide spectrum of users. As a result, these platforms have also become great resources to provide support for emergency management. During any crisis, it is necessary to sieve through a huge amount of social media texts within a short span of time to extract meaningful information from them. Extraction of topic keywords from these unstructured social media texts play a significant role in building any application for emergency management. Topic models have the ability to discover latent topics and latent feature representations of words in a document collection and can be used for this purpose. The main aim of this paper is twofold: to explore topic model implementations and look at its effectiveness on short messages (as short as 140 characters); and to perform an exploratory data analysis on short texts related to crises collected from Twitter, and look at different visualizations to understand the commonality and differences between topics and different crisis related data.
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探讨短文本的主题模型:以危机数据为例
近年来,Twitter和Facebook等社交媒体平台已成为广大用户获取信息的重要来源之一。因此,这些平台也成为为应急管理提供支持的重要资源。在任何危机中,都有必要在短时间内筛选大量的社交媒体文本,从中提取有意义的信息。从这些非结构化的社交媒体文本中提取主题关键词对于构建任何应急管理应用程序都具有重要作用。主题模型能够发现文档集合中单词的潜在主题和潜在特征表示,并可用于此目的。本文的主要目的有两个:探索主题模型的实现并观察其在短消息(短至140个字符)上的有效性;并对从Twitter上收集的与危机相关的短文本进行探索性数据分析,并通过不同的可视化来了解主题和不同危机相关数据之间的共性和差异。
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