Tweet Analysis Based on Distinct Opinion of Social Media Users’

Abstract

The state of mind gets expressed via Emojis’ and Text Messages for the huge population. Micro-blogging and social networking sites emerged as a popular communication channels among the Internet users. Supervised text classifiers are used for sentimental analysis in both general and specific emotions detection with more accuracy. The main objective is to include intensity for predicting the different texts formats from twitter, by considering a text context associated with the emoticons and punctuations. The novel Future Prediction Architecture Based On Efficient Classification (FPAEC) is designed with various classification algorithms such as, Fisher’s Linear Discriminant Classifier (FLDC), Support Vector Machine (SVM), Naïve Bayes Classifier (NBC), and Artificial Neural Network (ANN) Algorithm along with the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm. The preliminary stage is to analyze the distinct classification algorithm’s efficiency, during the prediction process and then the classified data will be clustered to extract the required information from the trained dataset using BIRCH method, for predicting the future. Finally, the performance of text analysis can get improved by using efficient classification algorithm.