husainburhanpurwala / emotion-based-music-recommender

Emotion-Based-Music-Recommender

An emotion-based music recommendation system is a type of algorithm or software that suggests music to a user based on their current emotional state. It uses data mining and machine learning techniques to analyze the emotional characteristics of music and match them to a user's current mood or emotions. The system may ask the user to input their current emotional state or may analyze data from various sources, such as social media activity or facial expressions, to determine the user's emotional state. The system then recommends music tracks that match the user's emotional state, based on the characteristics of the music such as tempo, rhythm, key, melody, and lyrics. For example, if a user is feeling sad, the system may suggest slow, melancholic music with sad lyrics. If the user is feeling happy, the system may suggest upbeat, energetic music with positive lyrics. Emotion-based music recommendation systems can be used in various applications, such as music streaming services, mobile apps, or even in-store music systems to create personalized playlists for users based on their emotional state. They can also be used in music therapy to help individuals with mental health issues manage their emotions and improve their mood.

Application Interface

App Screenshot - 1

App Screenshot - 2

Authors

• @HusainBurhanpurwala