Using relation selection to improve value propagation in a ConceptNet-based sentiment dictionary

Abstract

Sentiment analysis has become an important topic in natural language processing in recent years, and sentiment dictionaries are essential for research in this field. Concept-level sentiment dictionaries have broader coverage than word-based dictionaries, but they are still insufficient for real-world applications. In our previous work, we used a commonsense knowledge base (ConceptNet) as the foundation to build a larger dictionary. By propagating sentiment values from concepts with known values to empty concepts, we greatly enlarged our concept-level sentiment dictionary. In this work, we refine our previous method by adding relation selection and bias correction steps. Based on the assumption that concepts pass their sentiment values to their neighbors in different ways depending on the relations connecting them, we use sequential forward search to find the best combination of relations. We also propose a bias correction method that guarantees that the average deviation and standard deviation of sentiment values in the whole sentiment dictionary remain unchanged. We show that the strategy can improve the polarity accuracy by 3.7% and the Kendall τ distance by 17.3% relative to our previous method. Also, our experiment shows that the dictionary we constructed leads to better performance in the sentiment polarity classification task.