Efficient Sentimental Analysis Using Ensemble Learning for Big Data

Abstract

Abstract: Sentiment Analysis is a process of extracting useful patterns from textual data, these useful patterns include interpreting and classifying sentiment into: neutral, positive, or negative, from that data using certain analysis techniques. Field of sentiment analysis have got a lot of evolving and attention lately, it’s calling opinion mining also, it's interested to study people reviews, opinion, attitudes and evaluation. In this work, a sentiment prediction model for standford and Amazon data is proposed. The proposed model utilizes ensemble learning and clustering approaches for sentiment prediction. To make model suitable for real world and to handle big data, the model is deployed on Apache Spark. To test the suitability of proposed model in real world, the robustness and sensitivity of proposed model is be tested. Keywords: Time series forecasting, Sentiment Analysis, Hybrid Models