Date of Award

5-2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Engineering and Sciences

First Advisor

Thomas C. Eskridge

Second Advisor

Marius Silaghi

Third Advisor

Samuel Babu Sekar

Fourth Advisor

Brian A. Lail

Abstract

Music has long been recognised as a powerful tool for emotional regulation, yet existing music streaming platforms often fail to align song recommendations with a user's current emotional state. Moodify is a mood-based music recommendation system designed to bridge this gap by delivering personalised playlists that reflect how a user feels in real time.

This project presents the design, development, and evaluation of Moodify, a mobile application that leverages the Circumplex Model of Emotion to capture user mood through an intuitive two-dimensional valence-arousal interface. Rather than relying on text input or manual search, users plot their emotional state directly onto a mood wheel, enabling a more natural and expressive form of interaction. The system then connects to the Spotify API to analyse the user's listening history and preferences, generating a curated list of songs that are aligned with both their current mood and personal taste.

The development process followed a user-centred design methodology, incorporating iterative prototyping, usability testing, and feedback-driven refinement. Evaluation findings demonstrated that Moodify successfully matched music recommendations to user mood in the majority of test cases, with participants reporting high levels of satisfaction and ease of use.

This work contributes to the growing field of affective computing and Music Information Retrieval (MIR) by demonstrating how emotional input can be effectively integrated into a real-world recommendation pipeline. Future work may explore the incorporation of physiological sensors and machine learning models to further enhance mood detection accuracy and recommendation quality.

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Available for download on Saturday, June 06, 2026

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