SMS sentiment classification using an evolutionary optimization based fuzzy recurrent neural network.

Abstract

Sentiment analysis using the inbox message polarity is a challenging task in text mining, this analysis is used to differentiate spam and ham messages in mail. Polarity estimation is mandatory for spam and ham identification, whereas developing a perfect architecture for such classification is the hot demanding topic. To fulfill that, fuzzy based Recurrent Neural network-based Harris Hawk optimization (FRNN-HHO) is introduced, which performs post-classification over the classified messages (spam and ham). Previously the authors tried to classify the spam and ham messages from the collection of SMSs. But sometimes, the spam messages may incorrectly be classified within the ham classes. This misclassification may reduce the accuracy. The sentiment analysis process is performed over the classified messages to improve such classification accuracy. The spam and ham messages from the available data are classified using a Kernel Extreme Learning Machine (KELM) classifier. The sentiment analysis and classification based experimental evaluation is carried out using accuracy, recall, f-measure, precision, RMSE, and MAE. The performance of the proposed architecture is evaluated using threedifferent datasets: SMS, Email, and spam-assassin. The Area under the curve (AUC) of the proposed approach is found to be 0.9699 (SMS dataset), 0.958 (Email dataset), and 0.95 (spam assassin).