Enhanced Elman spike neural network based sentiment analysis of online product recommendation

Abstract

The major task of Natural Language Processing (NLP) is Sentiments Analysis (SA) or Opinion Mining (OM). It captures the opinion, trust, and feelings of every user’s about the related products to find whether the attitude of users is positive, negative, or neutral. By this, companies can make necessary changes in the product for customer satisfaction. The majority of the existing approaches on sentiment analysis have inaccurate and takes more time. Therefore, in this manuscript, an Enhanced Elman Spike Neural Network based Sentiment Analysis of Online Product recommendation is proposed (EESNN-SA-OPR). Here, filtering Collaborative (FC) and product to product (P–P) similarity are used as new recommendation systems. The aim of Collaborative Filtering is “to predict the best shops and product–product​ similarity is to predict the best products”. Initially, the datas are taken from Amazon reviews database. Then the input data is pre-processed. The trilateral smoothing filtering (TRSF) is used to remove the content which is no more needed and texts related filtering. After that, the features like manufacturing price (MRP), Manufacturing date (MFD), discounts, offers, ratings in qualities are extracted by using Dominant Gradient Local Ternary Pattern descriptor (DGLTPD) technique. At last, enhanced Elman spike neural network classifies the product recommendation as excellent, good, very good, bad and very bad. The proposed method is executed in MATLAB and its performance is analyzed under some performance metrics, like mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), accuracy, F-Score, recall and precision. The proposed EESNN-SA-OPR method provides 23.14%, 15.96%, 31.54% higher accuracy and 12.33%, 21.31%, 41.09% lower mean absolute error compared with the existing techniques, like Sentiment analysis of online product reviews utilizing DLMNN and future prediction of online product utilizing IANFIS (DLMNN-SA-OPR), a machine learning-based sentiment analysis of online product reviews with new term weighting along feature selection method (LSIBA-ENN-SA-OPR) and Sentiment Analysis on the Reviews of Online Product utilizing Optimized RNN-LSTM along Support Vector Machine (RNN-LSTM-SA-OPR) respectively.