Classification Techniques Used in Sentiment Analysis & Prediction of Heart Disease using Data Mining Techniques: Review

Abstract

Sentiment analysis uses data mining methods to extract information and data from the web through natural language processing. This consists of emotion artificial intelligent and text analysis. It basically helps in finding out the polarity of word data which is categorized into negative, positive and neutral. Sentiment extraction from data sources is a difficult task because some data sources may have unstructured format of data. In this review paper, we tried to summarize a number of classification techniques used in sentiment analysis stating some of their advantages and disadvantages, performance and their accuracy.In this paper, the various data mining techniques used for the prediction of the heart disease are discussed. With the help of data mining, it is very easy task to make expert system where this plays an important role in the prediction of the health related problems. This helps in solving threat of heart related issues also. Data mining is the extraction of hidden predictive information from large databases which creates enhanced knowledge in the field of pharmaceutical science which helps to predict heart disease. Various data mining techniques are applied here. It produces fast, straightforward assessment of the distinct prediction prototype with the help of Artificial Intelligent techniques.