A path-based model for emotion abstraction on facebook using sentiment analysis and taxonomy knowledge

Each term in a short text can potentially convey emotional meaning. Facebook comments and shared posts often convey human biases, which play a pivotal role in information spreading and content consumption. Such bias is at the basis of human-generated content, and capable of conveying contexts which shape the opinion of users through the social media flow of information. Starting from the observation that a separation in topic clusters, i.e. sub-contexts, spontaneously occur if evaluated by human common sense, this work introduces a process for automated extraction of sub-context in Facebook. Basing on emotional abstraction and valence, the automated extraction is exploited through a class of path-based semantic similarity measures and sentiment analysis. Experimental results are obtained using validated clustering techniques on such features, on the domain of information security, over a sample of over 9 million page users. An additional expert evaluation of clusters in tag clouds confirms that the proposed automated algorithm for emotional abstraction clusters Facebook comments compatibly with human common sense. The baseline methods rely on the robust notion of collective concept similarity.