DIABETES TWITTER ANALYSIS USING IMPROVED ENSEMBLE MACHINE LEARNING TECHNIQUES

Nowadays, sentimental analysis plays an important role in healthcare domain. Diabetes is one of the healthcare problem should be analyzed to understand people's sentiments. This paper represents a novel improved ensemble classifier (IEC) to analyze tweets in twitter. Nearly, 145713 tweets are collected to analyze the performance of the algorithm. Numerous machine learning techniques are applied to analyze the classification performance. This approach detects to classify tweets using proposed ensemble method. The effectiveness of the proposed algorithm is compared with state-of-the art approaches such as bagging, boosting and stacking.