Valence-arousal analysis for mental-health document retrieval

Abstract

The increasing incidence of depression has attracted increased attention to mental-health document retrieval techniques which aims to help individuals efficiently locate documents and resources relevant to their depressive problems. However, current retrieval systems generally have low accuracy. We propose combining a Valence-Arousal-based (VA-based) retrieval model and other word-based retrieval models to improve the precision of retrieval results. The VA-based retrieval model considers affective words extracted from queries, which help provide a better understanding of user queries. Experimental results demonstrate that the combined methods outperform the word-based retrieval models which adopt word-level information alone, such as vector space model and BM25 model.