The era of music nowadays is getting bigger and bigger, including artists, music producers, and stakeholders. Music plays an important role in human life. It is one of the most enjoyable human experiences. It can influence our mood, emotions, motivation, and activity. We listen to music and experience emotions in the absence of any situations that cause us to feel joy, despair, or excitement. People can utilize music to express their identity and ideals to others as well as relax the body because brain waves can coordinate with the rhythm of a song. Fast or intense music can make individuals feel energized, while slow music might relax them.
Every music producer and artist expects to have their songs on the top charts everywhere. The Billboard Chart is the music industry standard record chart in the United States for songs. To be on the charts, the charts are ranked by the number of gross audience impressions computed by cross-referencing the exact times of radio airplay. This chart rated the most popular songs, regardless of performer, according to Billboard’s weekly nationwide survey of record and sheet sales, disk jockey, and jukebox performances.
The hit songs analysis employing a diverse set of machine learning algorithms on the data has provided valuable insights into the dynamics of music performance. We uncovered distinct patterns within the vast array of songs through clustering, revealing inherent groupings based on shared audio features and contextual characteristics. Association rule mining illuminated intricate relationships between words in the lyrics by using song lyrics data. Naive Bayes, Decision Tree, and Support Vector Machine models demonstrated their prowess in predicting hit or non-hit status, each bringing unique strengths to the forefront. These models not only showcased their accuracy in discerning patterns within Spotify data but also offered interpretable and actionable information for artists, producers, and industry stakeholders.
The use of clustering allowed for the identification of commonalities and differences among songs, assisting in genre categorization and trend analysis. Association rule mining can help identify frequent combinations of words or themes within song lyrics. This assists artists and producers in understanding themes in songs and aligning their creative content with popular topics. Naive Bayes, Decision Trees, and Support Vector Machines provided robust classification capabilities, aiding in accurately identifying potential hit songs.
Music producers and streaming platforms can derive several benefits from the study of hit song analysis. Music producers can use the insights gained from machine learning models to optimize the production process. Understanding the audio features and patterns associated with hit songs can guide producers in creating music that aligns with current trends and audience preferences. Moreover, producers and streaming platforms that effectively apply machine learning models gain a competitive advantage. The ability to predict hit songs and offer targeted recommendations enhances user engagement, attracting and retaining a larger audience. By understanding user clusters, streaming platforms can provide highly personalized music recommendations. Clusters represent groups of users with similar tastes, allowing for more accurate and targeted song suggestions that align with the preferences of each segment.
In conclusion, the application of machine learning techniques to Spotify data for hit songs analysis has opened new avenues for understanding the multifaceted nature of musical success. By leveraging clustering, association rule mining, Naive Bayes, Decision Trees, and Support Vector Machines, we have contributed to the evolving dialogue within the music industry. As technology advances and data sources expand, the potential for further refinement and innovation in hit songs analysis remains vast, promising continued insights into the intricate world where music and data converge.
Napathsawan Poopaiboon 