LAUSR

In the field of e-commerce applications, nowadays the aspect-based sentiment analysis has become vital and every consumer started focusing on various aspects of the product before making the purchase decision through online portals like Amazon, Walmart, Alibaba, Flipkart, etc. Hence, the enhancement of sentiment classification considering every aspect of product and services is in the limelight. In this proposed research, aspect-based sentiment classification model has been developed employing sentiment whale optimized adaptive neural network (SWOANN) for classifying the sentiment of key aspects of products and services. The proposed work uses the key features such as the positive opinion score, negative opinion score and the term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. Moreover, the computational speed and accuracy of sentiment classification of the product and services have been improved by the optimal selection of weights of the neurons of proposed model. The promising results are obtained by analysing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed model can be compatible with any sentiment classification problem of products and services.