Leveraging AI for Multidimensional Sentiment Analysis to Automate Customer Feedback Response within Salesforce CRM

Sonmez, Barbaros (2024) Leveraging AI for Multidimensional Sentiment Analysis to Automate Customer Feedback Response within Salesforce CRM. Masters thesis, Dublin, National College of Ireland.

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Abstract

Today’s customer-centric industry has forced businesses of all sizes to gain insight into their customers. Sentiment analysis is an important tool for analyzing large amounts of consumer feedback and understanding customers. Classic sentiment analysis categorizes utterances as positive or negative, however multidimensional sentiment analysis provides more information about language, such as emotions. Salesforce, as a Customer-Relationship-Management (CRM) solution, includes consumer feedback in the form of reviews, emails, and comments, which could offer organizations with useful information to improve their service and products. The literature review shows that there are not enough studies on using machine learning to improve the capabilities of CRM systems. Improving Salesforce’s machine learning capabilities for multi-dimensional sentiment analysis on customer feedback data can lead to increased customer understanding. This study aims to answer the question of how multi-dimensional sentiment analysis could leverage customer feedback to create an automated response system within Salesforce CRM. For this purpose, Support Vector Machine (SVM), RoBERTa, and Electra models were employed to perform multi-dimensional sentiment analysis on the GoEmotions dataset. The best performing algorithm Electra was connected to Salesforce to establish a tailored and customized customer response. The implementation resulted in accurate multi-dimensional sentiment analysis and timely response to customers within Salesforce, significantly improving CRM capabilities.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Onwuegbuche, Faithful UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence H Social Sciences > HF Commerce > Marketing > Consumer Behaviour
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	02 Jul 2025 18:03
Last Modified:	02 Jul 2025 18:03
URI:	https://norma.ncirl.ie/id/eprint/8001

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