• K.Bansal
• A. Jain
• G.K.Singh
• S.K.Jagtap
• K.A.Pujari, A.S.Pandit

In today’s era, Social media is a computer-based technology that facilitates sharing ideas, thoughts, and information through virtual networks and communities, including Instagram, Facebook, Twitter, Linkedin, and even Youtube. Commenting has become a new form of written communication as it allows people to avoid being interrupted and express their ideas. People write many comments on social media (on private accounts and beyond them), which can help brands. Tracking social activity can help us to serve customers better. Due to this extensive usage, Tries reflects public sentiment by analysing the ideas expressed in these comments. The proposed method consists of two phases called the testing and training phase. For extracting the features, the sentiwordnet is used. The distance-based classifier is used to classify the features. Finally, analyses of the sentiments are done by polarities classified by the SVM classifier. Sentiment analysis is a kind of data mining. It aims to develop a functional classifier for accurate and automatic sentiment classification of an unknown comment stream. Sentiment analysis classifying comments (sentiments) according to their positive, negative, or neutral opinion.

Sentiment Analysis, Distance-Based Classifier, Emotion Detection.

"SMART SENTIMENT ANALYSIS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.611-617, August-2022, Available :http://www.jetir.org/papers/JETIRFP06109.pdf

"SMART SENTIMENT ANALYSIS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. pp611-617, August-2022, Available at : http://www.jetir.org/papers/JETIRFP06109.pdf