Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review

Abstract Big data analytics plays a major role in various industries using computing applications such as E-commerce and real-time shopping. Big data are used for promoting products and provide better connectivity between retailers and shoppers. Nowadays, people always use online promotions to know about best shops for buying better products. This shopping experience and opinion about the shopper’s shop can be observed by the customer-experience shared across social media platforms. A new customer when searching a shop needs information about manufacturing date (MRD) and manufacturing price (MRP), offers, quality, and suggestions which can only be provided by the previous customer experience. The MRP and MRD are already available in the product cover or label. Several approaches have been used for predicting the product details but not providing accurate information. This paper is motivated towards applying Machine Learning algorithms for learning, analysing and classifying the product information and the shop information based on the customer experience. The product data with customer reviews is collected from benchmark Unified computing system (UCS) which is a server for data based computer product lined up for evaluating hardware, support to visualization, software management. From the results and comparison, it has been found that machine learning algorithms outperform than other approaches. The proposed HRS system has higher values of MAPE which is 96% and accuracy is nearly 98% when compared to other existing techniques. Mean absolute error of proposed HRS system is nearly 0.6 which states that the performance of the system is significantly effective.