Identifying Twitter Topics Using K-Means Clustering and Association Rule Mining for Improved Insights

Abstract

The annual growth in social media users has led businesses to increasingly leverage these platforms for marketing, promotion, and addressing public complaints. Twitter, now known as X, stands out as one of the most widely used social media platforms. It serves as a forum for various opinions and complaints regarding services provided by businesses. This study focuses on analyzing public opinions related to Indihome services, as expressed on the @indihomecare Twitter account. These opinions range from expressions of support to complaints about internet services and Indihome's responses to these issues. This study employs a text clustering approach using the K-means algorithm on Twitter data, complemented by association rules to identify topics related to Indihome customer complaints. The optimal number of clusters is determined using the Elbow method, while Word Cloud visualizations are utilized to illustrate frequently occurring words within each cluster. The application of association rules revealed that the most frequently appearing words, with a support value of 0.057, were "indihome," "account," "whatsapp," and "channel." These findings provide insights into the primary concerns and communication channels used by Indihome customers on Twitter