A Pipeline for Measuring Brand Loyalty Through Social Media Mining
Paper i proceeding, 2021

Enhancing customer relationships through social media is an area of high relevance for companies. To this aim, Social Business Intelligence (SBI) plays a crucial role by supporting companies in combining corporate data with user-generated content, usually available as textual clips on social media. Unfortunately, SBI research is often constrained by the lack of publicly-available, real-world data for experimental activities. In this paper, we describe our experience in extracting social data and processing them through an enrichment pipeline for brand analysis. As a first step, we collect texts from social media and we annotate them based on predefined metrics for brand analysis, using features such as sentiment and geolocation. Annotations rely on various learning and natural language processing approaches, including deep learning and geographical ontologies. Structured data obtained from the annotation process are then stored in a distributed data warehouse for further analysis. Preliminary results, obtained from the analysis of three well-known ICT brands, using data gathered from Twitter, news portals, and Amazon product reviews, show that different evaluation metrics can lead to different outcomes, indicating that no single metric is dominant for all brand analysis use cases.

Machine Learning, Deep Learning, Social Business Intelligent, Customer Relationship Management

Författare

Hazem Samoaa

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Physical Systems

Barbara Catania

Università degli Studi di Genova

SOFSEM 2021: Theory and Practice of Computer Science.

978-3-030-67730-5 (ISSN) 978-3-030-67731-2 (eISSN)

Vol. 12607

47th International Conference on Current Trends in Theory and Practice of Computer Science
Bolzano, Italy,

Ämneskategorier

Annan data- och informationsvetenskap

Kommunikationsvetenskap

Mediateknik

Styrkeområden

Informations- och kommunikationsteknik

DOI

10.1007/978-3-030-67731-2_36

ISBN

9783030677305

Mer information

Senast uppdaterat

2021-03-22