Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter

Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some sentiment.Therefore,sentiment analysis is currently a hot topic of research.In particular,aspect-based sentiment analysis concerns the exploration of emotions,opinions and facts that are expressed by people,usually in the form of polarity.It is crucial to consider polarity calculations and not simply categorize reviews as positive,negative,or neutral.Currently,the available lexicon-based method accuracy is affected by limited coverage.Several of the available polarity estimation techniques are too general and may not reect the aspect/topic in question if reviews contain a wide range of information about different topics.This paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify ABSA-PER.Experimental outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.

Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction

In this investigation,we have shown that the combination of deep learning,including natural language processing,and conformal prediction results in highly predictive and efficient temporal test set sentiment estimates for 12 categories of Amazon product reviews using either in-category predictions,i.e.the model and the test set are from the same review category or cross-category predictions,i.e.using a model of another review category for predicting the test set.The similar results from in-and cross-category predictions indicate high degree of generalizability across product review categories.The investigation also shows that the combination of deep learning and conformal prediction gracefully handles class imbalances without explicit class balancing measures.

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Nowadays,commercial transactions and customer reviews are part of human life and various business applications.The technologies create a great impact on online user reviews and activities,affecting the business process.Customer reviews and ratings are more helpful to the new customer to purchase the product,but the fake reviews completely affect the business.The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information.Therefore,in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity.Here,Amazon Product Kaggle dataset information is utilized for investigating the customer review.The collected information is analyzed and processed by batch normalized capsule networks(NCN).The network explores the user reviews according to product details,time,price purchasing factors,etc.,ensuring product quality and ratings.Then effective recommendation system is developed using a butterfly optimized matrix factorizationfiltering approach.Then the system’s efficiency is evaluated using the Rand Index,Dunn index,accuracy,and error rate.

FEATURE NETWORK-DRIVEN QUADRANT MAPPING FOR SUMMARIZING CUSTOMER REVIEWS

With the rapid growth of e-commerce, customers increasingly write online reviews of the product they purchase. These customer reviews are one of the most valuable sources of information affecting selection of products or services. Summarizing these customer reviews is becoming an interesting area of research, inspiring researchers to develop a more condensed, concise summarization for users. However, most of the current efforts at summarization are based on general product features without feature＇s relationship. As a result, these summaries either ignore feedback from customers or do a poor job of reflecting the opinions expressed in customer reviews. To remedy this summarization shortcoming, we propose a feature network-driven quadrant mapping that captures and incorporates opinions from customer reviews. Our focus is on construction of a feature network, which is based on co-occurrence and sematic similarities, and a quadrant display showing the opinions polarity of feature groups. Moreover, the proposed approach involves clustering similar product features, and thus, it is different from standard text summarization based on abstraction and extraction. The summarized results can help customers better understand the overall opinions about a product.

Research of Order Allocation of E-commerce Logistics Service Supply Chain Considering Customer Evaluation

Based on the importance of customer evaluation for developing e-commerce enterprises,this paper analyzes the customer evaluation as a fuzzy variable and establishes a multi-objective mixed integer order allocation planning model by considering customer satisfaction,which maximizes customer praise and minimizes procurement cost.As the optimization goal,transaction cost is optimized for the order allocation of the secondary e-commerce logistics service supply chain.In order to defuzzify the customer evaluation,a fuzzy evaluation method is designed to transform the customer evaluation from fuzzy language evaluation to numerical measurement.Finally,the feasibility and effectiveness of the model are verified by using a specific example,and the order is made for the e-commerce enterprise.The allocation provides a theoretical reference.