A Human Word Association based model for topic detection in social
networks
- URL: http://arxiv.org/abs/2301.13066v2
- Date: Tue, 18 Jul 2023 11:39:22 GMT
- Title: A Human Word Association based model for topic detection in social
networks
- Authors: Mehrdad Ranjbar Khadivi, Shahin Akbarpour, Mohammad-Reza
Feizi-Derakhshi, Babak Anari
- Abstract summary: This paper uses the Concept of the Imitation of the Mental Ability of Word Association to propose a topic detection framework in social networks.
A special extraction algorithm has also been designed for this purpose.
The performance of this method is evaluated on the FA-CUP dataset.
- Score: 3.8137985834223507
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the widespread use of social networks, detecting the topics discussed in
these networks has become a significant challenge. The current works are mainly
based on frequent pattern mining or semantic relations, and the language
structure is not considered. The meaning of language structural methods is to
discover the relationship between words and how humans understand them.
Therefore, this paper uses the Concept of the Imitation of the Mental Ability
of Word Association to propose a topic detection framework in social networks.
This framework is based on the Human Word Association method. A special
extraction algorithm has also been designed for this purpose. The performance
of this method is evaluated on the FA-CUP dataset. It is a benchmark dataset in
the field of topic detection. The results show that the proposed method is a
good improvement compared to other methods, based on the Topic-recall and the
keyword F1 measure. Also, most of the previous works in the field of topic
detection are limited to the English language, and the Persian language,
especially microblogs written in this language, is considered a low-resource
language. Therefore, a data set of Telegram posts in the Farsi language has
been collected. Applying the proposed method to this dataset also shows that
this method works better than other topic detection methods.
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