A Pre-Trained Model for Aspect-based Sentiment Analysis Task: using Online Social Networking

Abstract

Aspect-based sentiment analysis (ABSA) is a crucial part of Natural Language Processing (NLP) that focuses on identifying emotions related to specific elements in written material. ABSA has gained widespread interest due to its ability to provide precise insights into sentiment expressions across different domains. Social media provides a valuable resource for ABSA, containing user-created content with viewpoints and feedback. However, the informal nature of social media text poses challenges for ABSA. This study investigates the performance enhancement of baseline and proposed models in the ABSA task context. Both baseline and proposed models were evaluated for accuracy and F1 score improvements. The results showed that the suggested model performs better than other baseline models, with an improvement of 0.52% F1 score and an overall accuracy increase of 1.16%. However, specific analyses for laptops indicated limitations in the proposed model's performance, with accuracy scores ranging from 72.65% to 84.98%.