CERM: Context-aware Literature-based Discovery Via Sentiment Analysis

Motivated by the abundance of biomedical publications and the need to better understand the relationship between food and health, we study a new sentiment analysis task based on literature- based discovery. Many attempts have been made to introduce health into recipe recommendation systems and food analysis. However, these methods focus highly on ingredient nutritional components or use simple computational models trained on curated labeled data. With the high availability of food and health information in biomedical texts but with a high cost of data labeling, there is a need for enhanced models to capture the intrinsic relationship between food ingredients and biomedical concepts, including genes, diseases, nutrition, and chemicals. This model should also utilize both available labeled resources and unlabelled data. In this paper, we propose a new sentiment analysis task called Entity Relationship Sentiment Analysis (ERSA) that focuses on capturing the relationship between biomedical and food concepts. This task extends the popularly studied Aspect Based Sentiment Analysis (ABSA) task. Specifically, our study focuses on the sentiment analysis of (entity-entity) pairs given a biomedical text sentence. This task presents a more significant challenge than traditional sentiment analysis tasks, as the sentiment expressed in the sentence may not necessarily reflect the sentiment of the relationship between two given entities in the sentence. Furthermore, we propose a semi-supervised architecture that combines two different types of word embeddings to encode the ERSA task better. The experimental results demonstrate that our proposed method consistently outperforms other semi-supervised learning methods across various learning scenarios.