A fuzzy-oriented sentic analysis to capture the human emotion in Web-based content

Abstract

Capturing the sentiments and the emotional states enclosed in textual information is a critical task which embraces a wide range of web-oriented activities such as detecting the sentiments associated to the product reviews, developing marketing programs that would be attractive for users, enhancing customer service with respect to its expectation until to identifying new opportunities and financial market prediction, besides managing reputations. Opinions and the emotions that are embedded in them, play a key role in decision-making processes, with different effects depending on the negative or positive valence of the mood. When the choice depends on some important features (i.e., time, money, reliability/efficacy, etc.) and on other opinions (which come from previous experience), could be crucial to make the best decision.Inferring opinions and emotions enclosed in the written language is a complex task which cannot rely on body languages (posture, gestures, vocal inflections), rather than discovering concepts with an affective valence. The role of opinions extracted by the social content is crucial to support consumers’ decision process; in addition, thanks opinions and emotions, it is possible to evidence improvements on existing decision supports and show how the opinion-mining techniques can be incorporated into these systems.This paper presents a tentative contribution that addresses this issue: it introduces a framework for extracting the emotions and the sentiments expressed in the textual data. The sentiments are expressed by a positive or negative polarity, the emotions are based on the Minsky’s conception of emotions, that consists of four affective dimensions, each one with six levels of activations [1]. Sentiments and emotions are modeled as fuzzy sets; particularly, the intensity of the emotions has been tuned by fuzzy modifiers, which act on the linguistic patterns recognized in the sentences. The approach has been tested on some sets of documents categories, revealing interesting performance on the global framework processing.