An Effectual Sentiment Analysis for High Classification Rates Using Medical Image Processing

Sentimental data is now a trend can be generally considered into two key types mainly facts and feelings. Facts are unbiased expressions around entities, actions, and their belongings. The thoughts of estimation in terms of sentiments are very extensive. In this paper, the main focus is given on the opinion terminologies that carry positive or negative thoughts. These thoughts are considered as sentiments. Plentiful work is done already using text processing in terms of mining of the information and recovery of the data. It is done using clustering approaches, mining of the text and other various text mining tasks but very less work is in handling of opinions in the medical field. Yet, sentiments are so imperative in the medical field to make decisions. The dataset on which the processing is done is the digital retinal DRIVE dataset was taken with 8-BPC (bits per color level) at 768 × 584 pixels. So this paper put light on the efficient approach for sentiment analysis using normalization and feature extraction for high classification rates and the simulation environment is used as MATLAB for development purpose.