Understanding Anonymous Social Media Posts using Topic Modeling

被引：0

作者：

Valencia, John Daniel M. ^{[1
]}

Laure, Al Joseph T. ^{[1
]}

Centino, Nino Mark R. ^{[1
]}

Fabito, Bernie S. ^{[1
]}

Imperial, Joseph Marvin R. ^{[1
]}

Rodriguez, Ramon L. ^{[1
]}

De la Cruz, Angelica H. ^{[1
]}

Octaviano, Manolito, V ^{[1
]}

Jamis, Marilou N. ^{[1
]}

机构：

[1] Natl Univ Manila, Coll Comp & Informat Technol, Manila, Philippines

来源：

2019 IEEE 11TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT, AND MANAGEMENT (HNICEM) | 2019年

关键词：

Topic Modeling; LDA; Social Media; Freedom Wall; LDA;

D O I：

10.1109/hnicem48295.2019.9072791

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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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