Enhance Twitter based Marketing by Identifying Topic Influencer Focus Rate

Abstract

Over the past few years the world has witnessed the evolution and advancement of the internet and the increase in social media accounts opened across platforms such as Twitter, Facebook, and Instagram, connecting people of different nationalities and demographics. This social media revolution has widespread influence across many aspects of mainstream society. One of these aspects is the business world and the customer relations maintained by the companies. On the environment created by the social media platforms, consumers can exchange ideas and give feedback on the different product and services consumed by them, to other potential customers. Evidence show that most social media users, use it to research products. Among those many depend on influencer accounts, and some would even make recommendations on social media based on their positive experiences. This report provides the design and development of a system which allows the marketing professionals to receive the information being looked for on a twitter influencer. Classification data mining technique is adopted to analyse the tweets further by classifying them into a focus area and calculating influencer features accordingly. These features are then displayed alongside the top influencers after being used to classify the twitter users as an influencer or not. Compared to existing systems, this system provide further details of the influencer which enables the marketing professionals to comparatively make a more informed decision when selecting an influencer to work with. This dissertation contains the entire process followed from initiation to evaluation of Topic Influencer Classifier.