Topic Discovery About Economy During COVID-19 Pandemic from Spanish Tweets
7th Future Technologies Conference, FTC 2022
; 561 LNNS:521-533, 2023.
Article
in English
| Scopus | ID: covidwho-2128475
ABSTRACT
Automatic topic discovery from natural language texts has been a challenging and widely studied problem. The ability to discover the topics present in a collection of text documents is essential for information systems. Topic discovery has been used to obtain a compact representation of documents for grouping, classification, and retrieval. Some tasks that can benefit from topic discovery recommendation systems, tracking misinformation, writing summaries, and text clustering. However, topic discovery from Spanish texts has been somewhat neglected. For this reason, this work proposes analyzing the behavior of topic discovery tasks in texts in Spanish, specifically in tweets about the Mexican economy during the COVID-19 pandemic, under three different approaches. A comparison was conducted, achieving promising results because the topic coherence metric indicates coherent topics. The highest score of 1.22 was obtained using PLSA with 50 topics, concluding that the topics encompassed the study domain. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
7th Future Technologies Conference, FTC 2022
Year:
2023
Document Type:
Article
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