Browsing by Author "Hossain, Rafayet"

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Applying text analytics on music emotion recognition
(Daffodil International University, 2018-05-07) Hossain, Rafayet; Sarker, Md. Rahmatul kabir rasel; Mimo, Mehejabin
Music is most important part in human life. When different kind of words are prepared a new sound which is enjoyable to the human beings, it is called music. Music is not just a source of entertainment. It is something more than entertainment. Title should come after the song is finished and should reproduce a termination of the lyrical content. In this paper our proposed system have recommend a proper song title based on song lyrics. We have applied topic model algorithm Latent Dirichlet allocation (LDA) for song title recommendation. In this paper another experiment is music emotion recognition. Song feel emotionally different to listeners depending on their lyrical contents. Emotions classify is so difficult through the existing music emotion classification method .We have extracted eight features from song lyrics. We propose a method for lyrics based emotion classification using feature selection. We also proposed another experiment music personality trait. We have to generate a customize dataset based on music interest and 20 questions of big five personality model. Our proposed module would be helpful for user. Song title recommendation system produces satisfactory result. We may use this module to recommend song title from lyrics. Music emotion recognition system will help to predict the overall emotional state of a user. Music personality traits could be useful to find out the personality measurements of any user.
BanglaMusicStylo: A Stylometric Dataset of Bangla Music Lyrics
(IEEE, 2018-12-03) Hossain, Rafayet; Marouf, Ahmed Al
With the rapid growth of Bangla music industry huge volume of Bangla songs are produced every day. Immense number of producers, lyricists, singers and artists are involved in production of songs from different genres. Among many genres of Bangla music; classical, folk, baul, modern music, Rabindra Sangeet, Nazrul Geeti, film music, rock music and fusion music has gained the highest popularity. Lyricists try to express their feelings and views towards any situation or subject through their writings. Therefore, each lyricist have their own dictionary of thoughts to put on music lyrics. In this paper, we have presented “BanglaMusicStylo”, the very first stylometric dataset of Bangla music lyrics. We have collected 2824 Bangla song lyrics of 211 lyricists in a digital form. All the lyrics are stored in text format for further use. This dataset could be used for stylometric analysis such as authorship attribution, linguistic forensics, gender identification from textual data, Bangla music genre classification, vandalism detection, emotion classification etc. Identifying the significant research opportunities in this area, we have formalized this dataset which could be used for stylometric analysis.
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.
Sentiment Analysis From Depression-related User-generated Contents from Social Media
(2021 8th International Conference on Computer and Communication Engineering (ICCCE), IEEE, 2021-07-01) Saha, Ananna; Marouf, Ahmed Al; Hossain, Rafayet
In this paper, we try to detect the sentiment levels such as positive, negative and neutral sentiments from depression related posts and comments generated in social media platforms. Social media platforms such as Facebook, Twitter are not only used for communication or building networks among connections, but also are getting useful for supporting needy peoples who are on special need or care in terms of mental support. In Facebook, there are several depression support groups, which are very much effective to provide mental support to the victims. In this paper, we try to formalize the depression-related posts and comments into a concise lexicon database and detect the sentiment levels form each instance. We have segmented the total work into two parts: sentiment detection and applying machine learning algorithms to analyze the ability to detect sentiment from such special category of texts. We have utilized python textblob package to detect the sentiment levels and applied traditional machine learning algorithms such as Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Sequential Minimal Optimization (SMO), Logistic Regression (LR), Adaboost (AB), Bagging (Bg), Stacking (St) and Multilayer Perceptron (MP) on the linguistic features. We have determined the precision, recall, F-measure, accuracy, ROC values for each of the classifiers. Among the classifiers Random Forest has outperformed others showing 60.54% correctly classified instance. We believe such sentiment analysis on special category of texts may lead to further investigation in natural language understandings.