Sentiment classification improvement using semantically enriched information

The emergence of new and challenging text mining applications is demanding the development of novel text processing and knowledge extraction techniques. One important challenge of text mining is the proper treatment of text meaning, which may be addressed by incorporating different types of information (e.g., syntactic or semantic) into the text representation model. Sentiment classification is one of the challenging text mining applications. It may be considered more complex than the traditional topic classification since, although sentiment words are important, they may not be enough to correctly classify the sentiment expressed in a document. In this work, we propose a novel and straightforward method to improve sentiment classification performance, with the use of semantically enriched information derived from domain expressions. We also propose a superior scheme for generating these expressions. We conducted an experimental evaluation applying different classification algorithms to three datasets composed by reviews of different products and services. The results indicate that the proposed method enables the improvement of classification accuracy when dealing with reviews of a narrow domain.