An Optimized Customers Sentiment Analysis Model Using Pastoralist Optimization Algorithm (POA) and Deep Learning

Abstract

Users usually express their sentiment online which influences purchased products and services. The computational study of people's feelings and thoughts on entities is known as sentiment analysis. The Long Short-Term Memory (LSTM) model is one of the most common deep learning models for solving sentiment analysis problems. However, they possess some drawbacks such as longer training time, more memory for training, easily over fits, and sensitivity to randomly generated parameters. Hence, there is a need to optimize the LSTM parameters for enhanced sentiment analysis. This paper proposes an optimized LSTM approach using a newly developed novel Pastoralist Optimization Algorithm (POA) for enhanced sentiment analysis. The model was used to analyze sentiments of customers retrieved from Amazon product reviews. The performance of the developed POA-LSTM model shows an optimal accuracy, precision, recall and F1 measure of 77.36%, 85.06%, 76.29%, and 80.44% respectively, when compared with LSTM model with 71.62%, 78.26%, 74.23%, and 76.19% respectively. It was also observed that POA with 20 pastoralist population size performs better than other models with 10, 15, 25 and 30 population size.