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Social Networking is the medium widely used for expressing emotions and opinions in public life through smart phones and other mediums on the Internet. Amongst the popular portals is the Twitter. Twitter has been the point of attraction to several people in research in important areas like prediction of democratic electoral events, consumer brands, movie collections at box office, stock market, celebrities etc. Opinion mining also called as sentiment analysis offers a fast and broader way of monitoring the public sentiments. In this paper, a study on various perspectives and approaches of Twitter data analysis being carried out in recent years using opinion mining is made by considering the words, retweets, hashtags and emoticons.

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Heart disorders, also known as Cardiovascular Diseases (CVDs), are one of the world's worst problems. They are invisible and strike without warning when the body's limits are reached. Every day, the health-care business generates a vast amount of data about patients and conditions. Researchers and workers, on the other hand, do not make proper use of this information. The healthcare sector today is rich in data but deficient in competence. There are a variety of data processing and machine learning approaches and tools available for extracting useful information from databases and using that information to make more accurate predictions and decisions. The main idea of this research paper is to summarize recent research on heart disease prediction with comparative results, as well as to draw analytical conclusions and reduce the amount of attributes utilized in heart disease diagnosis, which may result in fewer tests being required. that a patient must undergo. Our effort also aims to improve the proposed system's efficiency. Electrocardiograms are a non-invasive and low-cost approach to diagnose cardiac problems (ECG).