Ensemble approach for twitter sentiment analysis

Due to enlargement of social network and online marketing websites. The Blogs and reviews of the user are acquired from these websites. And these become useful for analysis and Decision making for various types of products, marketing and movie etc. with the extent of the usefulness of social Reviews. It is to be needed carefully analysis of that data. There are various techniques and methods are available that can accurately analyses the social information and provides greater accuracy for the analysis. But one of the major issues available with the social media data is that data is unstructured and noisy. It is to be required to solve this problem. So here in this paper a framework is proposed that includes latest data preprocessing techniques instead of noise removal like stemming, Lemmatization and Tokenization. After Pre-Processing of data ensemble methods is applied that increase the accuracy of previous classification algorithms. This method is inherent from bagging concept. First apply Decision Tree, Kneighbor and Naive Bayes classifier that not provide batter accuracy after that boosting concept is applied with the help of AdaBoost method that improves the accuracy of previous classical classifiers. At last our proposed ensemble method ExtraTree classifier is applied that inherent from bagging concept. Here we use the Extra Tree classifier that take the various sample are taken from training set and various random trees are created. It is also called as extremely randomized tree that provides extreme refined view. So that, it is to be conveying that The ExtraTree classifier of bagging ensemble method outperforms than all other techniques that are previously applied in this paper. with using some novel pre-processing techniques data that produced is more refined and that provides clean and pure base for the implementation of ensemble techniques. And also contributes in improving the accuracy of the applied methods.