Dynamic Topic Modeling Using Social Network Analytics

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Tabassum, S (Author) Other; João Gama (Author) FEP; Azevedo, P (Author) Other; Teixeira, L (Author) Other; Martins, C (Author) Other; Martins, A (Author) Other

Conference proceedings International

Title: Progress in artificial intelligence: 20th EPIA Conference on Artificial Intelligence, EPIA 2021: Proceedings

Pages: 498-509

20th EPIA Conference on Artificial Intelligence, EPIA 2021

Virtual Event, September 7–9, 2021

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00V-GDV

DOI: 10.1007/978-3-030-86230-5_39

Abstract (EN): Topic modeling or inference has been one of the well-known problems in the area of text mining. It deals with the automatic categorisation of words or documents into similarity groups also known as topics. In most of the social media platforms such as Twitter, Instagram, and Facebook, hashtags are used to define the content of posts. Therefore, modelling of hashtags helps in categorising posts as well as analysing user preferences. In this work, we tried to address this problem involving hashtags that stream in real-time. Our approach encompasses graph of hashtags, dynamic sampling and modularity based community detection over the data from a popular social media engagement application. Further, we analysed the topic clusters¿ structure and quality using empirical experiments. The results unveil latent semantic relations between hashtags and also show frequent hashtags in a cluster. Moreover, in this approach, the words in different languages are treated synonymously. Besides, we also observed top trending topics and correlated clusters. © 2021, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific