• MD Saddam Hussain
• Shital Gupta

The data which comes from e-commerce site is unstructured text data, Text mining is becoming an important field in research for finding valuable information from unstructured texts. Data which contains an unstructured text which stores large amount of valuable information cannot simply be used for further processing by computers. Therefore, we need an exact processing methods, techniques and algorithms in order to extract this meaningful information which is done by using text mining. Classification of these user opinions is an Information Extraction and Natural Language Processing task that classify the user opinions into various categories like in the form of positive or negative. In this paper we can build a classifier’s based on Support Vector machine (SVM), Naive Bbayes (NB) and decision tree (CART) classification algorithm to identify the opinions and classify them onto categories and we can compute the performance measure of these classifiers and also compare the classification algorithms performance based on their accuracy and we can say that CART algorithm performed better as compared to SVM and NB. In this we can also classify the real time tweets review into various emotions and polarity through decision tree classification model.

web data, text mining, data mining, R, text mining techniques, SVM, Decision tree, classification, Naive Bayes, CART

"REAL TIME SENTIMENT CLASSIFICATION OF TWITTER DATA USING SVM, NAIVE BAYES & CART ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.82-87, June 2019, Available :http://www.jetir.org/papers/JETIR1908164.pdf

"REAL TIME SENTIMENT CLASSIFICATION OF TWITTER DATA USING SVM, NAIVE BAYES & CART ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp82-87, June 2019, Available at : http://www.jetir.org/papers/JETIR1908164.pdf