Sentiment Analysis of Online Crowd Input towards Brand Provocation in Facebook, Twitter, and Instagram

Abstract

In this paper, we undertake sentiment analysis from netnography data to understand the need for giving special attention to sentiments expressed by the online crowd towards brand via the social media platform. The understanding of this will explain the emotions of the brand community subscribers towards the brand as the result of the interaction established with the brand online. This study utilizes a qualitative approach in which the input given by the brand community subscriber from three chosen social media platforms were analysed using AYLIEN, Text Analysis API and Monkeylearn software to extract sentiment polarities based on Positive, Negative, Sarcastic, Ideology and Neutral sentiments. The outcome shows that the provocation sentiment needs to be managed efficiently in order to trigger interaction within and between the online crowd and brand community subscribers for sustaining a long-term relationship over the social media platform for effective brand communication strategies.