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Exploring the Emotional Landscape: Sentiment Analysis of Prince's Lyrics

Sentiment analysis of Prince's lyrics unveils intriguing insights into the emotional tone of his music. Through tokenization and lexicon-based analysis, we explore which sentiments prevail and how they vary across songs and decades. This assignment not only delves into the technical aspects of sentiment analysis but also prompts reflection on its broader implications in understanding the intersection of artistry, emotion, and societal trends.

Problem Description

This sentiment analysis assignment focuses on Prince's lyrics dataset, obtained from scraping Billboard Chart information and various lyric sources. The goal is to gain insights into the sentiment expressed in the lyrics and understand how it varies across different dimensions.

  1. Load the Required Libraries and Read the Data
  2. The necessary libraries are loaded to facilitate data analysis. The Prince lyrics dataset is read from a CSV file.

  3. Tokenize and Preprocess Text, Calculate Match Ratio
  4. Text preprocessing involves tokenizing Prince's lyrics and calculating match ratios with lexicons. This step aims to identify which lexicon contains more distinct words from the lyrics.

  5. Sentiment Analysis and Visualization
  6. Sentiment analysis is implemented using the "nrc" lexicon. Visualization techniques are employed to understand which words contribute most to the sentiment scores, categorized by sentiment.

  7. Song with the Most Positive Words
  8. Identification of the song with the highest proportion of positive words, considering the total number of words in each song.

  9. Sentiment Analysis for "Sign O’ the Times"
  10. Analyzing the sentiment of the song "Sign O' the Times" using the "nrc" lexicon and visualizing the results.

  11. Polarity by Chart Level
  12. Breaking down the analysis to the chart level using the "bing" lexicon. The analysis includes creating a graph of polar sentiment per chart level, considering positive and negative sentiments.

  13. Polarity by Decades
  14. Further breakdown of the analysis by decades using the "bing" lexicon. A graph is created to illustrate polar sentiment per decade, providing insights into sentiment trends over time.

  15. Reflection on the Assignment
  16. Reflection on the learnings from the assignment, emphasizing the significance of sentiment analysis in extracting subjective information from text. The potential applications of sentiment analysis are discussed, ranging from customer satisfaction and brand perception to market trends, political sentiment, and crisis management.

    What else do you want to know using this dataset? The exploration could extend to correlating sentiment with significant events in Prince's life. Understanding how sentiment in lyrics aligns with his life events could offer deeper insights into the emotional aspects of his music. Additionally, analyzing sentiment trends across different albums or periods of his career could provide a more comprehensive understanding of the evolution of his musical expression.