Time-Series Clustering for Determining Behavioral-Based Brand Loyalty of Users Across Social Media

Document Type

Article

Source of Publication

IEEE Transactions on Computational Social Systems

Publication Date

11-14-2022

Abstract

In recent years social media data analytics allow enterprises to adopt a data driven approach to manage their processes. Although social media provide a plethora of data, still much work needs to be done to transform these data into services that businesses can use and make insightful decisions. In this work, we address one of the most important problems in business, the relationship with the customers and more precisely the identification of loyal customers. We use behavioral analytics to model and process customer actions. We extract user behavior based on our unified crawling approach and collect data from three different social media namely Reddit, Twitter and YouTube. We are extracting for each user three different behaviors namely communication, sentiment and product mix and convert them into a 3-D time-series. We use shapelet clustering to determine the loyal users. To verify our approach, we develop a set of metrics based on trust, commitment and engagement and we show that our approach results in differentiating the loyal users successfully. Moreover, we validate our results presenting a word cloud visualization. We extend our methodology introducing a semantic data transformation algorithm where we use the topic extraction, reducing the time-series to a more relevant one. Our experiments show that based on the verification metrics, our transformation increases the accuracy of the clustering results.

ISSN

2329-924X

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

PP

Issue

99

First Page

1

Last Page

15

Disciplines

Computer Sciences

Keywords

Social networking (online), Behavioral sciences, Data mining, Media, Business, Measurement, Clustering algorithms

Indexed in Scopus

no

Open Access

no

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