• Akshay Yadav
• Ajay Varma
• Rohan Raut
• Mrs. D. M. Gohil

With over 317 million active users a month, Twitter has become a treasure of data for those making an attempt to know however folks feel regarding topics, brands and more. the most aim behind sentiment analysis (or opinion mining) is to find out feeling, perspective and opinion from a natural text. In twitter sentiment analysis, we have a tendency to distribute tweets into positive, neutral or negative. Clustering may be a procedure within which identically resembled objects square measure classified together and type a pack or cluster. we have a tendency to conducted a study and located out that the utilization of cluster will quickly and efficiently distinguish tweet son the premise of their sentiment scores and may find weekly and powerfully positive or negative tweets once clustered with results of different dictionaries. during this project we have a tendency to shall implement an online service which is able to cluster a twitter knowledge, analyze the sentiments/opinions and presents how to find relationships between the tweets on the premise of polarity and judgement. Lastly the classification are going to be displayed on a web site concerning the popularity and neutrality of the tweets or opinions.

Stress detection, factor graph model, micro-blog, social media, healthcare, social interaction

"Opinion Analysis of Twitter Data Using Bayesian Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.617-620, May-2019, Available :http://www.jetir.org/papers/JETIR1905J93.pdf

"Opinion Analysis of Twitter Data Using Bayesian Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp617-620, May-2019, Available at : http://www.jetir.org/papers/JETIR1905J93.pdf