Interested in learning more about the used datasets?
Check out the datasets I created: Top 5 Songs Dataset from Each Generation
Check out the dataset I created: Historical Events Dataset

The analysis begins by categorizing songs into different generations, allowing you to examine how musical trends have evolved over time. This segmentation helps you understand the cultural influences and musical preferences that shaped each era.

• Understanding Sentiment in Lyrics: By performing sentiment analysis on song lyrics, you gain insights into the emotional content of the music. This aspect is particularly fascinating as it allows you to uncover the prevailing sentiments of each generation, whether it's the optimism of the Boomer era or the introspection of the Millennial generation.
• Visualizing Musical Attributes: Radar charts provide a visually compelling way to compare the musical attributes of different genres. Through these charts, you can see how genres vary in terms of danceability, energy, liveness, speechiness, and valence, offering a nuanced understanding of each genre's sonic characteristics.
• Identifying Genre Trends: The analysis also includes an exploration of genre trends, revealing which genres were most prevalent across different generations. This aspect allows you to trace the rise and fall of musical genres over time, shedding light on the ever-changing landscape of popular music.
• Word Frequency Analysis: Delving into the lyrics of songs from the Boomer generation, you analyze word frequency to uncover the most common themes and sentiments expressed in the music of that era. This textual analysis provides a deeper understanding of the cultural and societal themes that resonated with audiences during that time.

Explore the full analysis on my GitHub repository, including the R Studio code and accompanying images.