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We have always enthusiastic to the trending technologies and also future science.However we should also known to the fact that earth is formed about 4.5 years billion years ago and first well equipped and brain develop man appeared 35000 years ago,But the journey from earth formed to now a days need statistical and probability distribution to store data and predict the things,opinion and many more.We are living in 21st Century,In which Human are very close to various devices, mobiles, laptops, tabs and other gadgets which generate huge volume of data and Microservices based web applications running on these have made it simpler for us to get any kind of data at any time and from any place daily. Social media platforms are also used for expressing our opinions for the products and services. The estimation and ranking of millions of the social site users can be collated to extract their perspective and sentiment towards any products or services and use that information for future market and business improvement or domain analysis.Hence the foremost thing is that to predict the things on the basis of data and analysis of behavior of products.In this paper, an open source approach is presented which we have collected and stored tweets from Twitter API and then pre-processed, analyzed,processed and visualized these tweets using R programming. To interpret sentiments of tweets we are utilizing a statistical tool, R programming. This sentiment analysis is based on text data retrieval from streamed web and then classifying human perspectives in eight distinct classifications of feeling (dislike, fear, anger, indication, sadness, trust,happy) and two unique sentiments (positive and negative). We present a new promotion vector for catalogue the tweets as positive, negative and extract human’s opinion about products.


Natural Language Processing (NLP), Sentiment Analysis,Twitter, Statistical Data and R Programming

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How to Cite
Miss Seema Sheikh and Prof. Archana Vyas, “TEXT CLASSIFICATION AND CLUSTERING OF SOCIAL DATA BY COMPUTATIONAL INTELLIGENCE APPROACH”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 3, p. 5, Apr. 2020.