An emotion-driven approach for aspect-based opinion mining

The remarkable ability to understand the opinion of a user about a specific topic of discussion allows intelligent systems to provide more specific and personalized suggestions especially when no other information is available. The strategies for opinion mining, also known as sentiment analysis, are in last years topic of in-depth studies. In this work, we present an approach of text mining for detecting the topic of discussion for textual contents and the emotion that the writer feels while writing it. Conversely to the classic strategies of sentiment analysis, we enrich the standard polarity prediction task with more fine-grained information about user’s emotion. By using this information, the final behavior of the personalized system could be designed by taking into account the view about the topic of the specific user. For performing this task, we adopted a hybrid approach which uses both lexicons and semantic representation of sentences for the operation of aspect classification. Training data for the aspects detection module have been extracted from already categorized last year world news. The emotional labeling approach is, instead, based on the posts left by users on Facebook, which have been annotated using the emoticon encountered. The evaluation has been conducted on a dataset of tweets opportunely collected using hash-tags which refer both to the topic of discussion and the emotional opinion.