Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Gopinath, S.

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    Performance analysis of multiple classifiers using different term weighting schemes for sentiment analysis
    (Institute of Electrical and Electronics Engineers Inc., 2019) Anees, A.A.; Prakash Gupta, H.; Dalvi, A.P.; Gopinath, S.; Mohan, B.R.
    Information sharing and review platforms has generated large volumes of opinionated data which is usually in unstructured form. With the help of Sentiment Analysis, this data can be transformed into structured data which can be useful for commercial applications such as product reviews and feedback, marketing analysis, etc. The purpose of this work is to analyzes the performance of three classifiers(SVM, Naive Bayes, and Logistic Regression) with respect to providing positive or negative sentiment for three different scenarios(Movie Reviews, Election Opinions, and Food Reviews). The three classifiers are compared using fixed set of preprocessing steps and four different weighting schemes(Term frequency inverse document frequency (TFIDF), Term frequency inverse class frequency (TFICF), Mutual Information (MI), and X2 statistic (CHI)). The controlled experimental results showed that Logistic Regression classifier performs better in terms of overall accuracy when MI is used as weighting scheme. © 2019 IEEE.
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    Bayesian optimization and gradient boosting to detect phishing websites
    (Institute of Electrical and Electronics Engineers Inc., 2021) Pavan, R.; Nara, M.; Gopinath, S.; Patil, N.
    We propose an Extreme Gradient Boosting framework for classification and regression problems emerging in machine learning for small-sized data sources sampled from a discrete distribution, i.e. data containing discrete or quantized attributes. The model parameters are iteratively refined from a prior belief for specific use cases using Bayesian optimization. We focus the application area of this framework on detecting fraudulent websites. With properly stated reasoning, we empirically test our methodology on a publicly available and bench-marked UCI Phishing dataset to demonstrate the superior performance of this approach as compared to existing methods in the literature. © 2021 IEEE.