A Generic NLI approach for Classification of Sentiment Associated with Therapies
This work addresses sentiment analysis in medical texts for researchers and practitioners, but it is incremental as it applies an existing method to a specific shared task.
The paper tackled sentiment classification for therapies by using a natural language inference approach with transformer models, achieving a 75.22% F1-score, which was 11% above the mean and 4% above the median of other submissions.
This paper describes our system for addressing SMM4H 2023 Shared Task 2 on "Classification of sentiment associated with therapies (aspect-oriented)". In our work, we adopt an approach based on Natural language inference (NLI) to formulate this task as a sentence pair classification problem, and train transformer models to predict sentiment associated with a therapy on a given text. Our best model achieved 75.22\% F1-score which was 11\% (4\%) more than the mean (median) score of all teams' submissions.