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Narrative in the NBA: Using Sentiment Analysis to Predict the League MVP
Trachtenberg, Reuben
Trachtenberg, Reuben
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Abstract
The rapid growth of social media platforms like Twitter provides data scientists with unprecedented access to opinions on various subjects. In predictive modeling, these stores of diverse opinions could enhance traditional formulas or offer an alternative perspective to shed light on the potential for sentiment analysis in the prediction space. This paper discusses an effort to quantify the importance of player narrative in the NBA MVP race, a feature often absent in many past prediction models, by performing sentiment analysis on Twitter data. Using Python tools and logistic regression modeling, we collected and analyzed tweets and advanced statistics spanning five NBA seasons. The results are presented graphically to show the feature importance of advanced and sentiment statistics. Following the results, we discuss the power of sentiment analysis and its potential in the prediction space moving forward.
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Date
2025-01-01