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This project builds a sentiment analysis model for music lyrics using R and R Shiny. We collect and label lyrics from Spotify and Genius APIs, clean the data, and use a Naive Bayes classifier with TF-IDF features. The model is deployed via R Shiny for interactive sentiment classification.
Leveraged NLP techniques such as sentiment analysis and topic modeling to analyze different stand-up comedians using LDA, lemmatization, markov models, etc.
Python scripts used to calculate 3 basic similarity measures, suitable for ad hoc information retrieval systems: Levenshtein Edit Distance, Jaccard, and a Term-Document matrix.