Knowledge-based identication of emotional status on social networks

A knowledge based methodology is proposed for the content understanding and sentiment identication of the shared comments in social networks. The goal of this work is to retrieve the sentiment information associated to an opinion and classify it by its polarity and sentiment by means of a semantic analysis. Our approach implements knowledge graphs, similarity measures, graph theory algorithms and disambiguation processes. The results obtained were compared with data retrieved from Twitter and users' reviews in Amazon. We measured the eciency of our contribution with precision, recall and F-measure comparing it with the traditional method of just looking up concepts in sentiment dictionaries which usually assigns averages. Moreover an analysis was carried out in order to nd the best performance for the classication by using polarity, sentiment and a polarity-sentiment hybrid . A study is presented for remarking the advantage of using a disambiguation process in knowledge processing.