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."
