Abstract
News is a worldwide resource that is always expanding. Manually categorising texts/data has become tiresome and unproductive as the volume of internet material continues to grow. As a result, there is a compelling need to organise this data systematically and in a personalised manner. This paper presents a case study on identifying MIZO news categories from the news articles collected from the ‘Zonet’ website. Those articles are divided into three categories, namely Tualchhung (local news), Ramchhung (national news) and Rampawn (international news). In this paper, various machine learning techniques, namely random forest, SVM, KNN, decision tree, Naïve Bayes and neural network, have been explored for the classification of Mizo news, and their accuracy is being measured. The result reflects the superiority of the SVM model over the other models.
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References
Pal, K., Patel, B.V.: A study of current state of work done for classification in Indian languages. Int. J. Sci. Res. Sci. Technol. 3(7), 403–407 (2017)
Kadhim, A.I.: Survey on supervised machine learning techniques for automatic text classification. Artif. Intell. Rev. 52(1), 273–292 (2019)
Hartmann, J., Huppertz, J., Schamp, C., Heitmann, M.: Comparing automated text classification methods. Int. J. Res. Mark. 36(1), 20–38 (2019)
Srividhya, V., Anitha, R.: Evaluating preprocessing techniques in text categorization. Int. J. Comput. Sci. Appl. 47(11), 49–51 (2010)
How, B.C., Narayanan, K.: An empirical study of feature selection for text categorization based on term weightage. In: IEEE/WIC/ACM International Conference on Web Intelligence (WI’04), pp 599–602. IEEE (2004)
Liu, C., Sheng, Y., Wei, Z., Yang, Y.-Q.: Research of text classification based on improved tf-idf algorithm. In: 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), pp. 218–222. IEEE (2018)
Akinyelu, A.A., Adewumi, A.O.: Classification of phishing email using random forest machine learning technique. J. Appl. Math. 2014 (2014)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45–66 (2001)
Rajaram, R., Balamurugan, A.: Suspicious e-mail detection via decision tree: a data mining approach. J. Comput. Inf. Technol. 15(2), 161–169 (2007)
Dai, W., Xue, G.-R., Yang, Q., Yong, Y.: Transferring Naive Bayes classifiers for text classification. In: AAAI, vol. 7, pp. 540–545 (2007)
Khamar, K.: Short text classification using kNN based on distance function. Int. J. Adv. Res. Comput. Commun. Eng. 2(4), 1916–1919 (2013)
Wang, T., Liu, L., Liu, N., Zhang, H., Zhang, L., Feng, S.: A multi-label text classification method via dynamic semantic representation model and deep neural network. Appl. Intell. 50(8), 2339–2351 (2020)
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Lalthangmawii, M., Das, R., Lalramhluna, R. (2023). Mizo News Classification Using Machine Learning Techniques. In: Bhateja, V., Yang, XS., Lin, J.CW., Das, R. (eds) Evolution in Computational Intelligence. FICTA 2022. Smart Innovation, Systems and Technologies, vol 326. Springer, Singapore. https://doi.org/10.1007/978-981-19-7513-4_50
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DOI: https://doi.org/10.1007/978-981-19-7513-4_50
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