An Empirical Prediction Methodology for the Emotional Behaviors with the Impact of Musical Features
Malini M1, Kripa Menon2, M.Soumya Krishnan3

1Malini M*, Integrated, Student, Amrita Vishwa Vidyapeetham University, Coimbatore.
2Kripa Menon, Student, Amrita Vishwa Vidyapeetham University, Coimbatore.
3M.Soumya Krishnan, Faculty Associate, Dept. of CS &IT, Amrita Vishwa Vidyapeetham, Amrita School of Arts and Sciences, Coimbatore.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 26, 2020. | Manuscript published on April 10, 2020. | PP: 646-649 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3336049620/2020©BEIESP | DOI: 10.35940/ijitee.F3336.049620
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Abstract: Music is the combination of melody, linguistic information and singer’s mental realm. As popularity of music increases, the choice of songs also varies according to their mental conditions. The mental conditions reach the supreme bliss to melancholy strain based on the musical notes. Majority mostly prefer songs, which satisfy their current state of mind. Pragmatic analysis in music by computer is a difficult task, as emotion is very complex and it camouflages the real situation. Hence, In this paper , trying to classify the songs based on the features of music which helps to classify the emotion more easily. Music feature extraction is done using Music Information Retrieval (MIR) toolbox. The dataset consists of 100 of Hindi songs of 30 seconds clip and later classify the emotion based on Naïve Bayes classification method using Weka API. 
Keywords: Data Mining, Naïve Bayes, MIR Toolbox, Weka Tool.
Scope of the Article: Data mining and warehousing