Aspect-Based Sentiment Analysis on Product Reviews

Abstract

The focus of this paper was on product reviews. The goal of this is to study two (NLP) for evaluating product review sentiment analysis. Customers can learn about a product's quality by reading reviews. Several product reviews characteristics, such as quality, time of evaluation, material in terms of product lifespan and excellent client feedback from the past, will have an impact on product rankings. Manual interventions are required to analyse these reviews, which are not only time-consuming but also prone to errors. As a result, automatic models and procedures are required to effectively manage product reviews. (NLP) is the most practical method for training a neural network in this era of artificial intelligence. First, the Naive Bayes classifier was used to analyse the sentiment of consumers in this study. The (SVM) has categorised user sentiments into binary categories. The goal of the approach is to forecast some of the most important characteristics of product reviews, and then analyse Customer attitudes about these aspects. The suggested model is validated using a large-scale real-world dataset gathered specifically for this purpose. The dataset is made up of thousands of manually annotated product reviews. After passing the input via the network model, (TF) and (IDF) pre-processing methods were used to evaluate the feature. Aspect-based sentiment analysis is also predicted using some approaches. The outcomes precision, recall and F1 score are very promising.