Schein AI, Caver JF, Honaker RJ, Martell CH (2010) Author attribution evaluation with novel topic cross-validation. Int J Digital Evid 4(1):1–13ĭe Vel O, Anderson A, Corney M, Mohay G (2001) Mining e-mail content for author identification forensics. J Am Soc Inf Sci Technol 60(3):538–556Ĭhaski CE (2005) Whos at the keyboard? authorship attribution in digital evidence investigations. Stamatatos E (2009) A survey of modern authorship attribution methods. Malheiro R, Panda R, Gomes P, Paiva RP (2018) Emotionally-relevant features for classification and regression of music lyrics.
Goienetxea I, Martínez-Otzeta JM, Sierra B, Mendialdua I (2018) Towards the use of similarity distances to music genre classification: a comparative study. Roblek D, Eck D (2018) Machine learning to generate music from text (July 5 2018) US Patent App. ACM Trans Interact Intell Syst (TiiS) 5(1), 4 Expert Syst Appl 60:190–210ĭeng JJ, Leung CH, Milani A, Chen L (2015) Emotional states associated with music: Classification, prediction of changes, and consideration in recommendation. J Am Soc Inf Sci Technol 57(3):378–393Ĭorrêa DC, Rodrigues FA (2016) A survey on symbolic data-based music genre classification. Zheng R, Li J, Chen H, Huang Z (2006) A framework for authorship identification of online messages: writing-style features and classification techniques. KeywordsĪlSallal M, Iqbal R, Palade V, Amin S, Chang V (2017) An integrated approach for intrinsic plagiarism detection. It is observed that the Naive Bayes (NB) classifier provides higher accuracy in comparison with the other methods as it shows 93.9, 85, and 86.7% of accuracy while considering the stop words for our three data sets, respectively. Six methods of machine learning were used for the author identification, and high accuracy had been achieved from these methods while applied to the data sets D2A, D4A, and D7A, which were built from Bengali song lyrics. It presents the first work on machine learning-based computational approach for author attribution from the lyrics of Bengali songs.
This paper demonstrates a guideline to identify the author of a Bengali song from the lyrics of that song using machine learning.
Authorship attribution is one of the ways of identifying the author from a linguistic corpus. As a result, author identification from songs, more specifically from lyrics, is an important and realistic possibility. Although songs are very popular among the enthusiasts, authors of them get little recognition. Bangladesh is no exception as it has a great history of music with a great tradition of song writings over centuries. Every country or region in the world has its own form and style of music. People listen to music both as a form of entertainment and means of relaxation.
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