The Last Thing We Forget: Applying Natural Language Processing to Decode Memories Evoked by Modern Music

Abstract

With the pinnacle of the digital era upon us, the widespread accessibility of streaming platforms has disrupted the music industry. Now, more than ever, music has the potential to fully embrace its role in uniting all listeners in experiencing an emotionally transformative force regardless of their geographical location, demographic background, language, or culture. This thesis analyzes the potential for modern music genres to evoke memories and shared experiences via two avenues. The first is an experimental research study conducted in the Princeton University Music Cognition Lab where participants recorded music-evoked autobiographical memories (MEAMs). The second is through an exploration of YouTube music video comment threads. After rigorous processing and sanitizing of this data, a series of Natural Language Processing (NLP) techniques surfaced thematic elements within listeners’ comments on modern music. Beginning with an introductory TF-IDF analysis and then migrating to more complex techniques like PCA dimension reduction, LDA topics analysis, and visualizing cosine similarity between the MEAMs and YouTube comments compared to their corresponding song lyrics, the results yielded fascinating themes with shared memories at both the song and genre levels. While applying NLP techniques to original data consisting of unconstrained, freely available responses is relatively uncharted territory in the digital music space, the findings were definitive and provide a basis for further analysis of this multi- dimensional data.