CERM: Context-aware Literature-based Discovery via Sentiment Analysis
Julio Christian Young Uchenna Akujuobi
Driven by the abundance of biomedical publications, we introduce a sentiment
analysis task to understand food-health relationship. Prior attempts to
incorporate health into recipe recommendation and analysis systems have
primarily focused on ingredient nutritional components or utilized basic
computational models trained on curated labeled data. Enhanced models that
capture the inherent relationship between food ingredients and biomedical
concepts can be more beneficial for food-related research, given the wealth of
information in biomedical texts. Considering the costly data labeling process,
these models should effectively utilize both labeled and unlabeled data. This
paper introduces Entity Relationship Sentiment Analysis (ERSA), a new task that
captures the sentiment of a text based on an entity pair. ERSA extends the
widely studied Aspect Based Sentiment Analysis (ABSA) task. Specifically, our
study concentrates on the ERSA task applied to biomedical texts, focusing on
(entity-entity) pairs of biomedical and food concepts. ERSA poses a significant
challenge compared to traditional sentiment analysis tasks, as sentence
sentiment may not align with entity relationship sentiment. Additionally, we
propose CERM, a semi-supervised architecture that combines different word
embeddings to enhance the encoding of the ERSA task. Experimental results
showcase the model's efficiency across diverse learning scenarios.