Affiliations: [a] Department of ECE, PSG College of Technology, Coimbatore, India
| [b] Department of CSE, KPR Institute of Engineering and Technology, Coimbatore, India
Correspondence:
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Corresponding author. G. Srivatsun, Associate Professor, Department of ECE, PSG College of Technology, Coimbatore, India. E-mail: vatsunpg@gmail.com.
Abstract: Writing is a crucial component of the language requirement and is an effective method for correctly reflecting language proficiency. Manually evaluating Tamil language exams becomes time-consuming and costly for standardized language administrators as they grow in popularity. Numerous studies on computerized English assessment systems have been conducted in recent years. Due to Tamil text’s complicated grammatical structures, less research has been done on computerized evaluation methods. In this research, we present a Tamil review comment analysis system using a novel multivariate naïve Bayes classifier (mv - NB) where the comments are acquired from an online social network and performed training using the database for further analysis. Experiments show that the graded Kappa of 0.4239, error rate of 2.55 and precision of 85% was achieved on the online dataset by our contents grading system, which is superior in grading compared to the other widely used machine learning algorithms training on big datasets. Our findings are promising. Additionally, our contents analysis may provide beneficial criticism on Tamil writing on YouTube posts including comments, spelling errors and morphological issues that help to analyze thelanguage correlation.
Keywords: Writing, Tamil content, grading system, reviews, morphological issues
