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Sentiment Analysis of Bengali Music based on various Audio Features: An analysis of Machine Learning and Deep Learning Methods

Published: 29 July 2024 Publication History

Abstract

Sentiment analysis is a method used to determine the emotional tone or mood conveyed in a text or work of literature. Music functions as a constructive medium for emotional expression, providing a powerful means to communicate and convey feelings. Recently, music sentiment analysis has emerged as a popular method for curating and recommending music to listeners based on their emotional state. Despite the abundant literary legacy of the Bengali language, there are only a limited number of notable works that effectively accomplish the desired objective, and the number of sentiment categories is quite low. Furthermore, these efforts rely exclusively on music lyrics, which may not always be an optimal approach. This is because many lines in a song may lack a literal meaning, making it challenging for classifiers to accurately assign them to the appropriate sentiment category. Furthermore, each song possesses inherent audio characteristics. Therefore, in this research, we propose a novel approach aimed to categorize music sentiments into five distinct classes by utilizing these fundamental audio characteristics. Furthermore, we utilized our own dataset to accomplish the desired outcome. We have employed machine learning and deep learning classifiers to accurately categorize the sentiments of Bengali music into appropriate groups. We used suitable metrics to assess the efficiency of our models. In addition, we have conducted an analysis to determine which intrinsic audio characteristics are most significant in relation to the sentiment categories. Furthermore, our models have demonstrated exceptional performance, with a peak accuracy of 76.79%.

References

[1]
[n. d.]. Decision Trees and Random Forests. https://towardsdatascience.com/decision-trees-and-random-forests-df0c3123f991 [Online; accessed 7-November-2023].
[2]
[n. d.]. Deep Neural Network (DNN). https://deci.ai/deep-learning-glossary/deep-neural-network-dnn/ [Online; accessed 7-November-2023].
[3]
[n. d.]. Exploring the Importance and Techniques of Hyperparameter Tuning in Machine Learning. https://www.linkedin.com/pulse/exploring-importance-techniques-hyperparameter-tuning-jagarlapoodi [Online; accessed 10-November-2023].
[4]
[n. d.]. Introduction to Feature Scaling: Normalizing and Standardization. https://www.enjoyalgorithms.com/blog/need-of-feature-scaling-in-machine-learning [Online; accessed 7-November-2023].
[5]
[n. d.]. Kernel Functions-Introduction to SVM Kernel and Examples. https://data-flair.training/blogs/svm-kernel-functions/ [Online; accessed 5-November-2023].
[6]
[n. d.]. KNN Algorithm: When? Why? How?https://towardsdatascience.com/knn-algorithm-what-when-why-how-41405c16c36f [Online; accessed 7-November-2023].
[7]
[n. d.]. Label Encoding in Python – 2024. https://www.mygreatlearning.com/blog/label-encoding-in-python/ [Online; accessed 7-November-2023].
[8]
[n. d.]. Multi-Layer Perceptron vs. Deep Neural Network. https://www.baeldung.com/cs/mlp-vs-dnn [Online; accessed 10-November-2023].
[9]
[n. d.]. Multilayer Perceptron Explained with a Real-Life Example and Python Code: Sentiment Analysis. https://towardsdatascience.com/multilayer-perceptron-explained-with-a-real-life-example-and-python-code-sentiment-analysis-cb408ee93141 [Online; accessed 7-November-2023].
[10]
[n. d.]. Practise Bangla at all levels of education. https://alerts.tbsnews.net/ [Online; accessed 10-November-2023].
[11]
[n. d.]. A Quick Introduction to KNN Algorithm. https://www.mygreatlearning.com/blog/knn-algorithm-introduction/ [Online; accessed 7-November-2023].
[12]
[n. d.]. What is hyperparameter tuning?https://www.anyscale.com/blog/what-is-hyperparameter-tuning [Online; accessed 10-November-2023].
[13]
[n. d.]. What is Stochastic Gradient Descent?https://h2o.ai/wiki/stochastic-gradient-descent/ [Online; accessed 7-November-2023].
[14]
Towkir Ahmed, M. Arfayet Alam, Rakesh Roshan Paul, Md. Tanvir Hasan, and Raqeebir Rab. 2022. Machine Learning and Deep Learning Techniques For Genre Classification of Bangla Music. In 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE). 1–6. https://doi.org/10.1109/ICAEEE54957.2022.9836434
[15]
Md. Afif Al Mamun, Imamul Kadir, AKM Shahariar Azad Rabby, and Abdullah Al Azmi. 2019. Bangla Music Genre Classification Using Neural Network. In 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART). 397–403. https://doi.org/10.1109/SMART46866.2019.9117400
[16]
Maliha Mahajebin, Mohammad Rifat Ahmmad Rashid, and Nafees Mansoor. 2023. Mood Classification of Bangla Songs Based on Lyrics. In Inventive Communication and Computational Technologies, G. Ranganathan, George A. Papakostas, and Álvaro Rocha (Eds.). Springer Nature Singapore, Singapore, 585–597.
[17]
Afif Al Mamun. 2020. Bangla Music Dataset. https://www.kaggle.com/afifaniks/bangla-music-dataset
[18]
Maraz Mia, Pulock Das, and Ahsan Habib. 2023. Verse-Based Emotion Analysis of Bengali Music from Lyrics Using Machine Learning and Neural Network Classifiers. International Journal of Computing and Digital Systems 13, 1 (2023), 1–10.
[19]
Devjyoti Nath, Anirban Roy, Sumitra Kumari Shaw, Amlan Ghorai, and Shanta Phani. 2020. Textual lyrics based emotion analysis of Bengali songs. In 2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 39–44.

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    CNIOT '24: Proceedings of the 2024 5th International Conference on Computing, Networks and Internet of Things
    May 2024
    668 pages
    ISBN:9798400716751
    DOI:10.1145/3670105
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    New York, NY, United States

    Publication History

    Published: 29 July 2024

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    Author Tags

    1. Audio Features
    2. Bengali Music
    3. Machine Learning
    4. Multi-Class Classification
    5. Music Information Retrieval
    6. Neural Networks
    7. Sentiment Analysis

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