Sentimental Analysis on Kannada Language Inscriptions Using Machine Learning Techniques

  • Authors

    • Sachhidanand Sidramappa Department of Computer Science, Rani Channamma University, Vidyasangam, Belgavi, India. Department of Computer Science, Residential Govt. First Grade College, Ghodampalli, Bidar India
    • Mallamma V. Reddy Department of Computer Science, Rani Channamma University, Vidyasangam, Belgavi, India.
    https://doi.org/10.14419/vh7haq41

    Received date: September 23, 2025

    Accepted date: October 23, 2025

    Published date: November 2, 2025

  • Machine Learning, Pre-Processing, Dataset, Sentiment Analysis, Classification

  • Abstract

    An inscription is a piece of writing that is etched on metal, coins, tombs, rocks, building walls, and other hard surfaces. The focus of this paper is classification and sentiment analysis of Kannada Language inscriptions. The Indian Constitution recognizes Kannada Language is the official language of Karnataka and it’s one of the nation's eight scheduled languages. The Kannada Language Inscriptions are classified based on the various kingdoms and kings. The purpose of Sentiment analysis is to determine people’s thoughts, emotions, and feelings. The emotions and feelings may be good, bad, or neutral. In practice, the labels in the sentimental analysis fall into one of three categories. It can be Positive, Negative, or Neutral.  In this study, the other categories like joy, fear, sad, anger, culture, and war are taken into consideration. This study more on sentiment analysis for Kannada language inscription, and the aim is to recognize the different sentimental sentences that are written on copper plates, stone surfaces, and walls, etc. To train the model, the Kannada Language Inscription text is in the form of sentences. The classification of the data set is done on training and testing data sets separately. The various machine learning classifiers are used, such as Logistic Regression and Linear SVC, SGD, Random Forest, K-Nearest Neighbors, Multinomial Naive Bayes, and Random Forest. The Results show compared the Linear SVC, SGD, and Random Forest are most effective compared to the other classifiers.

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  • How to Cite

    Sidramappa, S. ., & Reddy, M. V. . (2025). Sentimental Analysis on Kannada Language Inscriptions Using Machine Learning Techniques. International Journal of Basic and Applied Sciences, 14(7), 11-18. https://doi.org/10.14419/vh7haq41