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.

Product-harm crisis intelligent warning system design based on fine-grained sentiment analysis of automobile complaints

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Sentiment Level Evaluation of 3D Handicraft Products Application for Smartphones Usage

“…The authors of [32] generated a machine learning technique for multi-domains for e-commerce goods reviews and sentiment classification by gaining the average classification accuracy for cross-domain sentiment classification of 77.52% and average accuracy for domain-specific classification of 85.58%. The authors of [33] applied machine learning algorithms for identifying sentiment by big consumer review data for the experience in using e-commerce and real-time shopping. The ability of the system is effective, achieving accuracy close to 98%.…”

ML-based group method of data handling: an improvement on the conventional GMDH

“…Following the aforementioned works, this study aims to enhance the ability of GMDH to handle more complex relationships between inputs and outputs, which has not been considered before. Considering the reasonable results of ML models in different regression and pattern recognition applications [20][21][22][23][24][25][26][27][28][29][30], it is valuable for us to study whether the combination of ML models and GMDH leads to better performance. A modified version of the GMDH is proposed, in which the basic polynomial functions are replaced by ML models.…”