Abstract
Abstract: As everyone is free to give their opinions about people or products they buy on social media and E-Commerce platforms respectively So, Opinion Mining is extensively used for classifying the opinions into different polarities- positive and negative. Its use has significant overlap with the domain of Machine Learning. The objective of this paper is to compare two Supervised Learning Algorithms- Support Vector Machine (SVM) and Multinomial Naïve Bayes (MNB) and Opinion Mining on twitter dataset. Opinion Mining is a Natural Language Processing (NLP) task which aims to determine the views of people by identifying and extracting the data. The performances of both the models are evaluated and compared their accuracy, precisions, recall values and f scores. The measurement accuracy is measured by Confusion Matrix and ROC curve. In results, it is observed that SVM and MNB both show almost same performance when compared. Keywords: Confusion Matrix, Multinomial Naïve Bayes, Natural Language Processing, Opinion Mining ROC curve, Support Vector Machine
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