Uncovering Flat and Hierarchical Topics by Community Discovery on Word Co-occurrence Network

Uncovering Flat and Hierarchical Topics by Community Discovery on Word Co-occurrence Network

Topic modeling aims to discover latent themes in collections of text documents. It has various applications across fields such as sociology, opinion analysis, and media studies. In such areas, it is essential to have easily interpretable, diverse, and coherent topics. An efficient topic modeling tec...

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Bibliographic Details
Published in:Data science and engineering Vol. 9; no. 1; pp. 41 - 61
Main Authors: Austin, Eric, Makwana, Shraddha, Trabelsi, Amine, Largeron, Christine, Zaïane, Osmar R.
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01-03-2024
Springer Nature B.V
SpringerOpen
Subjects:
Algorithm Analysis and Problem Complexity
Algorithms
Artificial Intelligence
Chemistry and Earth Sciences
Community mining
Computer peripherals
Computer Science
Computers
Data mining
Data Mining and Knowledge Discovery
Database Management
Graphs
Hierarchical topics
Information networks
Mines
Modelling
Natural language processing
Physics
Research Paper
Statistics for Engineering
Systems and Data Security
Topic modeling
Verbal communication
Words (language)
Topic modeling
Information networks
Community mining
Hierarchical topics
Graphs
Natural language processing
Data mining
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Summary:Topic modeling aims to discover latent themes in collections of text documents. It has various applications across fields such as sociology, opinion analysis, and media studies. In such areas, it is essential to have easily interpretable, diverse, and coherent topics. An efficient topic modeling technique should accurately identify flat and hierarchical topics, especially useful in disciplines where topics can be logically arranged into a tree format. In this paper, we propose Community Topic, a novel algorithm that exploits word co-occurrence networks to mine communities and produces topics. We also evaluate the proposed approach using several metrics and compare it with usual baselines, confirming its good performances. Community Topic enables quick identification of flat topics and topic hierarchy, facilitating the on-demand exploration of sub- and super-topics. It also obtains good results on datasets in different languages.
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ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-023-00239-2