Songs Analysis
The purpose of this project is to analyze a given song and extract relevant information from it. In the music industry, understanding the factors that contribute to a song's popularity is crucial for artists, producers, and record labels. This project aims to analyze the influence of lyrics and musical features on the popularity of songs. By combining information from the Spotify Dataset, and the Billboard Hot 100 Dataset, and trying the proposed methods as below: Sentiment Analysis: Analyze song lyrics to determine sentiment and emotional content. Correlation Analysis: Examine correlations between audio features (from Spotify data) and chart performance data (from Billboard data).
Further analysis is on report.ipynb or final-report.pdf (https://htmlpreview.github.io/?https://github.com/nish-nm/SpotifySongsSentimentAnalysis/blob/main/project/final-report.html)
To replicate project please create an account on data.world and kaggle, you would need api keys for downloading datasets, otherwise you are free to use repective data files in ''/data/'' folder More instructions to setup data.world and kaggle api keys are at the respective urls: data.world and kaggle
Make sure to use setup Python v3.9.13 or above, use requirements.txt to install all dependencies alternatively you can also use pipenv to setup your Python enviournment.
project/
├── fetch_data.py -- fetches data, removes null values and deletes unneccesary files
├── pipeline.sh -- makes a fetch data pipeline
├── project-plan.md -- project plan readme file
├── report.ipynb -- contains analysis
├── test.sh -- implements test.py
└── tests.py -- implements unit tests for fetch_data pipeline Analyzing the Influence of Lyrics and Musical Features on Song Popularity
- To what extent do song lyrics, specifically sentiment, and themes, influence a song's chart performance?
- Are there specific musical features (e.g., tempo, danceability) that are correlated with a song's chart success on the Billboard Hot 100?
In the music industry, understanding the factors that contribute to a song's popularity is crucial for artists, producers, and record labels. This project aims to analyze the influence of lyrics and musical features on the popularity of songs. By combining information from the Spotify Dataset, and the Billboard Hot 100 Dataset, and trying the proposed methods as below: Sentiment Analysis: Analyze song lyrics to determine sentiment and emotional content. Correlation Analysis: Examine correlations between audio features (from Spotify data) and chart performance data (from Billboard data).
- Metadata URL: Billboard hot-100 with lyrics
- Data URL: https://data.world/tazwar2700/billboard-hot-100-with-lyrics-and-emotion-mined-scores/workspace/file?filename=Final+processed+dataset.xlsx
- Data Type: XLSX
Excel file provided on data.world, contains lyrics and songs in Billboard's top 100 from 1958 to 2020.
- Metadata URL: Spotify Dataset 1921-2020
- Data URL1: https://www.kaggle.com/datasets/yamaerenay/spotify-dataset-19212020-600k-tracks/download?datasetVersionNumber=1
- Data Type1: CSV (artists.csv)
- Data Type2: CSV (tracks.csv)
- Data Type3: JSON (dict_artists.json)
The Excel file provided on kaggle, contains 600k+ songs on the popular streaming platform Spotify and contains information about artists, their tracks, and Musical Features.