iamsiddharthasharma / lyricsable

Official Github Repo for Lyricsable

Lyricsable

Starting Approach of the Project

The application will have a user interface where the user can input a phrase or a sentence. Upon submitting the phrase, the backend, built using the MERN stack, will process the input using natural language processing techniques.

The backend will first use libraries like NLTK, spaCy, or Gensim to preprocess the input phrase, including tokenization, part-of-speech tagging, and Named Entity Recognition (NER) to identify relevant entities like persons, locations, and organizations.

Once the backend has the preprocessed phrase, it will use a data science algorithm to search a database of song lyrics to identify the songs where the input phrase has been used. The search algorithm could utilize techniques such as word embeddings or TF-IDF to match the input phrase with the lyrics in the database.

After identifying the relevant songs, the backend will return the song titles, artist names, and any other relevant metadata to the frontend. The frontend will display this information to the user in a visually appealing way, allowing them to explore the songs that contain the input phrase.

Overall, this MERN stack-based web application would be a useful tool for anyone interested in discovering songs that contain specific phrases or lyrics. It could be further improved by adding features like song previews, links to streaming platforms, and a user account system for saving favorite songs.

The Strategy Behind

In this implementation, we utilized NLP (Natural Language Processing) and data science algorithms to extract and analyze song lyrics from the Genius API. Firstly, we fetched lyrics from the API based on a given phrase. Then, we employed NLP techniques such as tokenization and stop word removal to process both the input phrase and the song lyrics. By comparing the tokens, we identified songs that contained the given phrase. This process utilized basic NLP algorithms to filter and match the tokens. The integration of data science allowed us to extract meaningful insights from the lyrics and perform analysis. While this implementation utilized fundamental techniques, it can be further enhanced by leveraging advanced NLP algorithms like word embeddings or named entity recognition for more accurate and sophisticated matching. Ultimately, this combined NLP and data science approach enabled us to identify songs that contain a specific phrase, providing valuable insights to users.