Understanding Anonymous Social Media Posts using Topic Modeling

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI:10.1109/HNICEM48295.2019.9072791

John Daniel M. Valencia, Al Joseph T. Laure, Niño Mark R. Centino, Bernie S. Fabito, Joseph Marvin Imperial, Ramon L. Rodriguez, Angelica De La Cruz, Manolito V. Octaviano, Marilou N. Jamis

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引用次数: 3

Abstract

Social Media holds a substantial amount of text data that can help organizations better understand their clients. For students of National University (NU) – Manila, Facebook serves as a medium to express their opinions and create topics for discussion that may generally speak about the University. Through Topic Modeling using Latent Dirichlet Allocation (LDA), various experiments were conducted to identify the topics discussed by the students based on the highest coherence score value obtained. From these experiments, a total of twenty (20) topics with Alpha and Beta values set to one (1) revealed the highest coherence. The topics were labeled and revealed interesting insights. Personal relationships and school-related concerns were the common topics posted on the two Facebook pages. To further improve the study, a chronological approach for topic modeling is recommended.

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使用主题建模理解匿名社交媒体帖子

社交媒体拥有大量的文本数据，可以帮助组织更好地了解他们的客户。对于国立大学(NU) -马尼拉的学生来说，Facebook是他们表达意见和创造讨论话题的媒介，这些话题通常都是关于大学的。通过使用潜狄利克雷分配(Latent Dirichlet Allocation, LDA)进行话题建模，根据获得的最高连贯分值进行各种实验来识别学生讨论的话题。从这些实验中，Alpha和Beta值为1(1)的共有20个主题显示出最高的一致性。这些话题被贴上了标签，并揭示了有趣的见解。个人关系和与学校有关的担忧是这两个Facebook页面上发布的常见话题。为了进一步完善研究，建议采用时间顺序的方法进行主题建模。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )

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