Optimizing Sentiment Big Data Classification Using Multilayer Perceptron

Abstract

Internet-based platforms such as social media have a great deal of big data that is available in the shapeof text, audio, video, and image. Sentiment Analysis (SA) of this big data has become a field ofcomputational studies. Therefore, SA is necessary in texts in the form of messages or posts to determinewhether a sentiment is negative or positive. SA is also crucial for the development of opinion miningsystems. SA combines techniques of Natural Language Processing (NLP) with data mining approachesfor developing inelegant systems. Therefore, an approach that can classify sentiments into two classes,namely, positive sentiment and negative sentiment is proposed. A Multilayer Perceptron (MLP)classifier has been used in this document classification system. The present research aims to providean effective approach to improving the accuracy of SA systems. The proposed approach is applied toand tested on two datasets, namely, a Twitter dataset and a movie review dataset; the accuraciesachieved reach 85% and 99% respectively.