Matteo Borzi
Automated Data Integration and Machine Learning for Enhanced Social Media Marketing Analysis.
Rel. Daniele Apiletti. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2023
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Abstract
In today's digital era, the abundance of data generated on social media platforms presents a valuable resource for extracting marketing insights. This thesis, born from an internship project at Mediamente Consulting s.r.l., addresses the pressing need of a customer ??for efficient data integration and automation in the context of social media marketing campaign analysis. The primary objective is to develop a robust Data Integration model to streamline the visualization of social media marketing campaign performance. The traditional approach of manually downloading and aggregating standard reports from YouTube and LinkedIn analytics tools is time-consuming and error-prone. To replicate the company workflow, our thesis outlines a comprehensive framework comprised of four key stages.
The initial stage, Data Ingestion, involves the extraction of data from various sources with the utilization of REST APIs provided by the respective social media platforms
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