Social media sentiment analysis based on affective-behavioural-coginitive model of attitudes

Abstract

Sentiment Analysis is the study of classifying a given text based on its sentiment (positive/ negative polarity) of the expression. Sentiment analysis is being widely used to analyse the public opinion towards a given entity. Today in Web 2.0, social media is a popular platform to express one’s opinions and beliefs. Therefore, researchers are keen on investigating how social media sentiment analysis can be improved to benefit interested entities. Most of the sentiment analysis research has been conducted on identifying the polarity (i.e.: positive, negative or neutral) and emotions (i.e.: happiness, sadness, disgust, anger, fear and surprise). Comparatively, less focus has been given to study how expressions can be classified based on psychological aspects of attitude. The objective of the proposed research is to move beyond the mere polarity, and to investigate whether we can get an in-depth understanding of the expressed attitude. For this, we have used the ABC (Affective, Behavioural and Cognitive) model of attitude introduced in consumer psychology. In this research a new dataset was compiled by extracting Tweets on a specific topic and manually annotating them based on the attitude by domain experts. This research discusses how existing tools and technologies of Sentiment Analysis can be applied for this problem domain. Various preprocessing and feature extraction techniques were evaluated against a set of machine learning algorithms including Ensemble and Deep Learning models. Additionally, this research aims to contribute to reduce the gap between machine learning and consumer psychology and thereby proving the possibility of applying machine learning across different domains.