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Multilingual Text Categorization and Sentiment Analysis: a Comparative Analysis of the Utilization of Multilingual Approaches for Classifying Twitter Data by George Manias; Argyro Mavrogiorgou; Athanasios Kiourtis; Chrysostomos Symvoulidis; Dimosthenis Kyriazis is a Computer Science article available to read on EtoBox.
What is Multilingual Text Categorization and Sentiment Analysis: a Comparative Analysis of the Utilization of Multilingual Approaches for Classifying Twitter Data about?
Text categorization and sentiment analysis are two of the most typical natural language processing tasks with various emerging applications implemented and utilized in different domains, such as health care and policy making. At the same time, the tremendous growth in the popularity and usage of social media, such as Twitter, has resulted on an immense increase in user-generated data, as mainly represented by the corresponding texts in users’ posts. However, the analysis of these specific data and the extraction of actionable knowledge and added value out of them is a challenging task due to the domain diversity and the high multilingualism that characterizes these data. The latter highlights the emerging need for the implementation and utilization of domain-agnostic and multilingual solutions. To investigate a portion of these challenges this research work performs a comparative analysis of multilingual approaches for classifying both the sentiment and the text of an examined multilingual corpus. In this context, four multilingual BERT-based classifiers and a zero-shot classification approach are utilized and compared in terms of their accuracy and applicability in the classificat
Who reads Multilingual Text Categorization and Sentiment Analysis: a Comparative Analysis of the Utilization of Multilingual Approaches for Classifying Twitter Data?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- George Manias; Argyro Mavrogiorgou; Athanasios Kiourtis; Chrysostomos Symvoulidis; Dimosthenis Kyriazis
- Publisher
- Springer Science and Business Media LLC
- Published
- 2023
- Language
- EN
- Field
- Computer Science (Physical Sciences)