Browsing by Author "Sarker, Md. Rahmatul Kabir Rasel"

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Recognizing Language and Emotional Tone from Music Lyrics Using IBM Watson Tone Analyzer
(Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2023, IEEE, 2019-10-17) Marouf, Ahmed Al; Hossain, Rafayet; Sarker, Md. Rahmatul Kabir Rasel; Pandey, Bishwajeet; Siddiquee, Shah Md. Tanvir
Music has a soothing impact on listener's mood and emotional states. Apart from the rhythm, sequence, instrumental effects on a song, lyrics could be considered as the most vital element. Lyricists' mood and affection towards a song while writing could be understand from the lyrics. Lyrics does have the elements of fictions such as language tone, language style, diction and voice are well maintained in music lyrics. Understanding the tone of a song both language and emotional tones are essential to develop different interactive applications. Music players, video repositories, video sharing sites could use the understandings to recommend next song to play according to the music interest or mood of the listeners. In this paper, we have investigated the possibilities to use IBM Watson Tone Analyzer, an API service to analyze language and emotional tones from song lyrics. We have extracted the features from a 300 English song dataset using the supported API service and formulated a machine learning methodology to classify the language tone (analytical, confident and tentative) and emotional tone (anger, fear, joy and sadness). For classification, we have applied different classifiers including Naïve Bayes, decision tree, random forest, sequential minimal optimization and simple logistic regression.
Recommendation Approach of English Songs Title Based on Latent Dirichlet Allocation Applied on Lyrics
(Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2019, IEEE, 2019-10-17) Hossain, Rafayet; Sarker, Md. Rahmatul Kabir Rasel; Mimo, Mehejabin; Marouf, Ahmed Al; Pandey, Bishwajeet
The significance of music has evolved due to the vast diversity of entertainment industry. Songs are the widely used entertainment segment that can influence directly to the heart of the listeners. Choosing a suitable title for a song is considered as a common problem faced by the music directors. As the title gives the first impression of the song and only by the title listeners usually decide whether they will listen to this song or not, thus makes it a challenging task to determine. Lyrics are the most influential part of a particular song apart from the tune, rhythm, fusion, singer, genre etc. In this paper, we propose an approach to estimate and recommend the title of the song based on its lyrics. We have applied Latent Dirichlet Allocation (LDA) to find the hidden or implied topic of the song. The output of the LDA algorithm provides scoring on the significant words, which are passed to an estimation process to generate a song title. The proposed approach was experimented on over 200 English songs database having vast diversity in genre. The approach could be evaluated by the existing song title and the evaluation process is same as any recommendation system.