• Shruti Kulkarni
• Anisaaara Nadaph
• Dr. Geetika Narang

Social networking has become a distinguished platform within the digital age. In sentiment analysis, categorizing tweets into polarity categories may be a common task. the foremost advanced approaches to the present drawback use supervised machine learning models that are learned victimization manually annotated examples. Twitter sentiment analysis permits businesses to trace public angle regarding their product and events in real time. The text pre-processing of Twitter information is that the initial stage in sentiment analysis. the bulk of surviving Twitter sentiment analysis focuses on extracting new sentiment parts. The goal of this analysis is to indicate the way to mix matter info from Twitter conversations with sentiment diffusion patterns to enhance sentiment analysis on Twitter information. thereto purpose, we tend to examine sentiment spreading by staring at a development referred to as sentiment reversal, and that we discover many intriguing properties of the sentiment reversals. Then taking into consideration the interactions between matter info in Twitter messages and the sentiment diffusion patterns, we tend to gift SentiDiff, associate degree unvarying system for predicting sentiment polarity explicit in Twitter tweets. To our data, this can be the primary study to use sentiment diffusion patterns to help within the improvement of Twitter sentiment/emotion analysis. intensive tests on real-world information show that, in comparison to progressive matter information based on sentiment analysis algorithms

Twitter, sentiment analysis, text pre-processing, Diffusion of sentiment, social networks, feature fusion, and graph analysis.

"Twitter Sentiment Analysis using Supervised Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.i420-i424, May-2022, Available :http://www.jetir.org/papers/JETIR2205963.pdf

"Twitter Sentiment Analysis using Supervised Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppi420-i424, May-2022, Available at : http://www.jetir.org/papers/JETIR2205963.pdf