In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data.
Represents the first comprehensive review of Sentic Computing, state-of-the-art approach to opinion mining and sentiment analysis (see http://en.wikipedia.org/wiki/Sentiment_analysis) A special chapter on cognitive and affective modeling for natural language understanding Includes tips on different strategies (techniques, online resources, datasets, etc.) to opinion mining and sentiment analysis