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

Abstract

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

FADOHS: Framework for Detection and Integration of Unstructured Data of Hate Speech on Facebook Using Sentiment and Emotion Analysis

“…This graph bar summarizes the results of the experiments with mildly negative posts, moderately negative posts, and highly negative posts. The experiment with moderate negative posts overpasses the other two experiments, with more defined topics(17) and fewer undefined topics(5).…”

The impact of sentiment analysis from user on Facebook to enhanced the service quality

“…While Balahur and Turchi [7] defines it as the task of detecting, extracting, and classifying opinions and sentiments concerning different topics, as expressed in textual input. The desires and personal attractiveness of customers stem from an emotion that arises either positively or negatively in which it provides an essential factor in determining a decision as in [8] if the content of the user comment gets detail, which means more abstract less, would be produced more emotions on it, based on the evidence occurred. As stated in [9], for some cases, there is a time when the candidatespeople communicate channel utilized Facebook as their other communication media in Mexico.…”

Community elicitation from co-occurrence of activities

“…The problems connected to the modelling of explicit interactions have been already widely explored in literature. Facebook posts and comments have been examined to understand the role of the social network in information diffusion [9], to elicit the semantic relatedness [17,40], to determine the emotions conveyed by the posts [59,39], and many other. Twitter social connections have been used to find the sentiment polarity of posts [96], to discriminate from true and fake followers [23], and so on.…”

“…Another qualitative element of the relationship between users includes the sentiment and emotion conveyed by the posts. The purpose of this research is to analyse the content of Facebook posts and their comments to classify the users behaviour in respect to sentiment and emotion [39].…”