"I’ve Got a Feeling": Performing Sentiment Analysis on Critical Moments in Beatles History

Abstract

Our project involved the use of optical character recognition (OCR) and sentiment analysis tools to assess popular feelings regarding the Beatles and to determine how aggregated sentiment measurements changed over time in response to pivotal events during the height of their musical career. We used Tesseract to perform optical character recognition on historical newspaper documents sourced from the New York Times and smaller publications, leveraging advances in computer vision to circumvent the need for manual transcription. We employed state-of-the-art sentiment analysis models, including VADER, TextBlob, and SentiWordNet to obtain sentiment analysis scores for individual articles (Hutto and Gilbert 2014; TextBlob, n.d.; Baccianella, Esuli, and Sebastiani 2010). After selecting articles mentioning the group, we examined the changes in average sentiments displayed in articles corresponding to critical moments in the Beatles’ musical career to determine the impact of these events.

Keywords

• Beatles
• sentiment analysis
• optical character recognition
• historical newspaper archives
• artificial intelligence
• AI