Year: 2025
Type: article
Abstract: Natural language processing is one of the most trending fields in research, with sentiment analysis being one of the well-known problems in the field. Many methods have been proposed to handle text-based sentiment data, with social networks acting as one of the main data sources and research targets. An important step in designing a text-based model is the embedding method, which helps in the representation of the inputs. This study presents a novel static text embedding method to represent text inputs and compares its sentiment classification performance with some well-known text embedding methods. The results are on par with existing embedding methods, achieving a promising classification accuracy of 90.66%.
DOI: 10.48084/etasr.10098
Authors: G. R. Kishore, B. S. Harish, C. K. Roopa
Institution: JSS Science and Technology University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $431
OpenAlex ID: W4409174570