A Topic Modeling Method for Analyzes of Short-Text Data in Social Media Networks

Ian Macedo Maiwald Santos, Luciana de Oliveira Rech, Ricardo Moraes
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Abstract

Currently, many short texts are published online, especially on social media platforms. High impact events, for example, are highly commented on by users. Understanding the subjects and patterns hidden in online discussions is a very important task for contexts such as elections, natural disasters or major sporting events. However, many works of this nature use techniques that, despite showing satisfactory results, are not the most suitable when it comes to the short texts on social media and may suffer a loss in their results. Therefore, this paper presents a text mining method for messages published on social media, with a data pre-processing step and topic modeling for short texts. For this paper, we created a data set from real world tweets related to COVID-19 that is openly available1 for research purposes.
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社交媒体网络短文本数据分析的主题建模方法
目前,许多短文本都是在网上发布的,尤其是在社交媒体平台上。例如,高影响力事件会受到用户的高度评价。了解在线讨论中隐藏的主题和模式对于选举、自然灾害或重大体育赛事等情况来说是一项非常重要的任务。然而,许多这种性质的作品使用的技术,尽管表现出令人满意的效果,但并不是最适合的,当涉及到社交媒体上的短文本时,可能会遭受损失。因此,本文提出了一种针对社交媒体上发布的消息的文本挖掘方法,并对短文本进行了数据预处理和主题建模。在本文中,我们从与COVID-19相关的真实推文中创建了一个数据集,该数据集可公开获取1用于研究目的。
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ARCH-COMP23 Category Report: Hybrid Systems Theorem Proving ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Linear Continuous Dynamics ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics ARCH-COMP23 Repeatability Evaluation Report ARCH-COMP23 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants
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