Affective common sense knowledge acquisition for sentiment analysis

Abstract

Thanks to the advent of Web 2.0, the potential for opinion sharing today is unmatched in history. Making meaning out of the huge amount of unstructured information available online, however, is extremely difficult as web-contents, despite being perfectly suitable for human consumption, still remain hardly accessible to machines. To bridge the cognitive and affective gap between word-level natural language data and the concept-level sentiments conveyed by them, affective common sense knowledge is needed. In sentic computing, the general common sense knowledge contained in ConceptNet is usually exploited to spread affective information from selected affect seeds to other concepts. In this work, besides exploiting the emotional content of the Open Mind corpus, we also collect new affective common sense knowledge through label sequential rules, crowd sourcing, and games-with-a-purpose techniques. In particular, we develop Open Mind Common Sentics, an emotion-sensitive IUI that serves both as a platform for affective common sense acquisition and as a publicly available NLP tool for extracting the cognitive and affective information associated with short texts.