Sentiment Analytics for Monitoring and Analyzing Fan Page Posts

Abstract

One of the most significant ways to increase brand awareness or brand popularity in digital
marketing is by connecting them directly with consumers via social media using fan pages. Fan
pages allow consumers or users to interact with each other, discuss opinions, and create interactive
dialogue engagement among the virtual community. This kind of active communication is
preferred compared to websites that tend to do passive viewing of brand content. Public figures or
personal brands use fan pages too to increase their popularity. Through fan pages, public figures
establish an enduring and strong connection based on ongoing efforts to activate mutual
interactions, shared values, rewards, experimental contents, positive actions, and others. An active
and well-organized fan page will attract new visitors or new fans each day. This implies the
extensive awareness of branding popularity and competitiveness which are driven by fan page and
consumers. This paper studies the usage of sentiment analysis techniques to understand
consumers’ preferences for different types of posts on a fan page. The sentiment analysis measures
fan page’s effectiveness and analyzes metrics like calculate engagement rate, number of comments
or shares, or likings in fan pages and others. The results of sentiment analysis are visualized and
expected to advice on the next strategy or moves to increase the fans’ responsiveness. In this paper,
the authors have analyzed data collection from Sina Weibo by scrapping data from webpages using
URL, cookies, and user-agent based data. Webpage inspection and crawling were performed using
mobile view and program implementation using Python, R languages and Tableau.