• Anuradha Tanaji Khade
• Shweta Sambhaji Ghorpade
• Rohit Nandkumar Bhosale
• Rushikesh Ramesh Kesare
• Mrs.Hafsa Shoyeb Majgaonkar

In recent days, invention of new platforms in social media has given lot of boost to the business development. In the business process social media is playing an important role as a deciding factor for success or failure of a business in a growing economy of the country. One such platformwhich helps people to understand and gauge the business prospectus is twitter. In this paper we are addressing the problem of sentiment analysis in twitter; which mainly deals with classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. Analyzing the public sentiment is important for many applications such as firms trying tofind out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. The aim here isto develop a functional classifierfor accurate and automatic sentiment classification of an unknown tweet stream.

: In recent days, invention of new platforms in social media has given lot of boost to the business development. In the business process social media is playing an important role as a deciding factor for success or failure of a business in a growing economy of the country. One such platformwhich helps people to understand and gauge the business prospectus is twitter. In this paper we are addressing the problem of sentiment analysis in twitter; which mainly deals with classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. Analyzing the public sentiment is important for many applications such as firms trying tofind out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. The aim here isto develop a functional classifierfor accurate and automatic sentiment classification of an unknown tweet stream.

"Sentiment Analysis Of Text By Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.k214-k220, June-2023, Available :http://www.jetir.org/papers/JETIR2306A26.pdf

"Sentiment Analysis Of Text By Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppk214-k220, June-2023, Available at : http://www.jetir.org/papers/JETIR2306A26.pdf