Music recommendation system based on emotions in users social media behaviour

Abstract

" The modern lifestyle makes people more competitive. It can lead to more stressful situations in our lifestyle. With the changes in human emotional behaviour, they tend to share their feelings on social media platforms rather than communicating with relatives. Studies proved that people used to listen to music to avoid emotional situations in their life. But there is no proper way to get the most accurate music to listen to and avoid emotional conflicts. Resolving these conflicts, the music recommendation system based on emotion introduced. It analyses the users' recent social media content and detects the various kind of emotions. To ensure that the suggested music is relevant to users emotions, the lyrics analysing was done using natural language processing techniques to identify the music emotions. Most people pay attention to the meaning of the songs, that was the major reason to consider the emotions of the lyrics. The research was considered on a language-independent platform for both English and Sinhala. A labelled emotional dataset was chosen to evaluate the model. Using the English emotional detection model it was achieved a higher accuracy level than the Sinhala module. The researcher found that if music features can be added to consideration of the emotion detections for lyrics, It can be more accurate to recommend the songs. Furthermore, research revealed that there is no proper way to identify the exact emotion categories in lyrics due to different lines can give a different set of sentiment value. Therefore, the lyrics were categorised into positives and neutral to combined with the user’s emotions. The researchers emphasize that the detection of complex emotion categories could be done using a more accurate dataset and by adding more music features. The outcome of the recommendation system proved that the recommended songs are relevant to identified emotion categories."