Opinion Mining and Product Review Summarization in E-Commerce

2020 ◽  
pp. 216-243
Author(s):  
Enakshi Jana ◽  
V. Uma
Keyword(s):  
User Experience ◽  
Opinion Mining ◽  
Online Shopping ◽  
Online Reviews ◽  
The Internet ◽  
Product Reviews ◽  
Product Review ◽  
Document Summarization ◽  
Review Summarization ◽  
Product Manufacture

With the immense increase of the number of users of the internet and simultaneously the massive expansion of the e-commerce platform, millions of products are sold online. To improve user experience and satisfaction, online shopping platform enables every user to give their reviews for each and every product that they buy online. Reviews are long and contain only a few sentences which are related to a particular feature of that product. It becomes very difficult for the user to understand other customer views about different features of the product. So, we need accurate opinion-based review summarization which will help both customers and product manufacture to understand and focus on a particular aspect of the product. In this chapter, the authors discuss the abstractive document summarization method to summarize e-commerce product reviews. This chapter has an in-depth explanation about different types of document summarization and how that can be applied to e-commerce product reviews.

A Method of Extracting Opinion from Chinese Product Review

2011 ◽  
Vol 219-220 ◽  
pp. 1513-1517
Author(s):  
Rui Liu ◽  
Yi An ◽  
Lang Song
Keyword(s):  
Opinion Mining ◽  
Online Reviews ◽  
Product Reviews ◽  
Product Review ◽  
Sentiment Lexicon ◽  
Target Attribute

Automatic opinion mining and summarization from online reviews are very useful for customers and merchants. This paper proposes a method to extract opinions from Chinese product reviews. Firstly, reviews are pre-processed and the sentiment features are extracted based on a sentiment lexicon. Then, it finds out the matching target attribute using the extracted sentiment features base on the using co-occurrence knowledge of topic feature and sentiment feature. After the opinions were found, it generates the summary for products according to the most common opinions.

scholarly journals Product Review Mining for Opinion Using Ensemble Classifier

2013 ◽  
Vol 2 (2) ◽  
Author(s):  
G. Vinodhini ◽  
RM. Chandrasekaran
Keyword(s):  
Support Vector Machine ◽  
Opinion Mining ◽  
Online Reviews ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Product Reviews ◽  
Operating Characteristics ◽  
Product Review ◽  
Review Mining ◽  
Suitable Form

Online product reviews is considered as a major informative resource which is useful for both customers and manufacturers. The online reviews are unstructured-free-texts in natural language form. The task of manually scanning through huge volume of review is very tedious and time consuming. Therefore it is needed to automatically process the online reviews and provide the necessary information in a suitable form. In this paper, we dedicate our work to the task of classifying the reviews based on the opinion, i.e. positive or negative opinion. This paper mainly addresses using ensemble approach of Support Vector Machine (SVM) for opinion mining. Ensemble classifier was examined for feature based product review dataset for three different products. We showed that proposed ensemble of Support Vector Machine is superior to individual baseline approach for opinion mining in terms of error rate and Receiver operating characteristics Curve.   Key words: Opinion, Classification, Machine Learning.

scholarly journals A Semi-supervised Approach for Opinion Mining using Online Product Review

2018 ◽  
Vol 7 (4.5) ◽  
pp. 143
Author(s):  
Bhagyashree G. Bhongade ◽  
Ashwini V.Z
Keyword(s):  
Opinion Mining ◽  
Online Shopping ◽  
Product Information ◽  
Online Reviews ◽  
Product Reviews ◽  
Immediate Feedback ◽  
Online Product Review ◽  
Timely Fashion ◽  
Alignment Process

The growth of the internet as a secure online shopping channel has developed since 1994. With the Increasing number of e-commerce portal, we are now heavily inclined to online shopping. One of the benefits of online shopping is the ability to read reviews about the product purchased. This paper presents a semi-supervised approach for opinion mining using online product reviews obtained from   Amazon website. A semi-supervised model regards identifying opinion relation as an alignment process and gives more precision in comparison to unsupervised model. Opinion mining of online reviews is needed for first-hand assessments of product information and direct supervision of their purchase actions. Manufacturers can obtain immediate feedback and opportunities to improve the quality of their products in a timely fashion.  

Product Quality Assessment Based on Online Reviews

2019 ◽  
Vol 11 (3) ◽  
pp. 81-97
Author(s):  
Chao Li ◽  
Jun Xiang ◽  
Shiqiang Chen
Keyword(s):  
Quality Assessment ◽  
Product Quality ◽  
Opinion Mining ◽  
Online Reviews ◽  
Product Reviews ◽  
Product Features ◽  
Related Quality ◽  
Quality Dimensions

Reviews can reflect the degree of consumers' satisfaction and views on product quality, and consumers tend to read product reviews and then get helpful information about product quality before placing an order in e-commerce platforms. However, the existing research mainly focus on the assessment of review quality, fake review detection, opinion mining, and there is little research to assess product quality from the perspectives of product features based on reviews objectively and quantifialy. Therefore, the authors propose a method to assess product quality based on reviews in a granularity of product feature. The authors define the related quality dimensions and develop the corresponding assessment models, assess the review quality crawled from an e-commerce platform, then extract product features and opinion words from the quality reviews, and finally assess product quality on the extracted and consumer-concerned features. Experiment results demonstrate the methodology can achieve the assessment of product quality on any feature objectively and quantificationally.

scholarly journals An Affordance-Based Online Review Analysis Framework

2019 ◽  
Vol 1 (1) ◽  
pp. 2457-2466
Author(s):  
Tianjun Hou ◽  
Bernard Yannou ◽  
Yann Leroy ◽  
Emilie Poirson
Keyword(s):  
Opinion Mining ◽  
Online Reviews ◽  
User Preferences ◽  
Data Driven ◽  
Online Review ◽  
Product Reviews ◽  
Analysis Framework ◽  
Review Analysis ◽  
Classical Models ◽  
Feedback Data

AbstractOne of the main tasks of today's data-driven design is to learn customers' concerns from the feedback data posted on the internet, to drive smarter and more profitable decisions during product development. Feature-based opinion mining was first performed by the computer and design scientists to analyse online product reviews. In order to provide more sophisticated customer feedback analyses and to understand in a deeper way customer concerns about products, the authors propose an affordance-based online review analysis framework. This framework allows understanding how and in what condition customers use their products, how user preferences change over years and how customers use the product innovatively. An empirical case study using the proposed approach is conducted with the online reviews of Kindle e-readers downloaded from amazon.com. A set of innovation leads and redesign paths are provided for the design of next-generation e-reader. This study suggests that bridging data analytics with classical models and methods in design engineering can bring success for data-driven design.

scholarly journals Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach

2022 ◽  
Vol 3 (4) ◽  
pp. 283-294
Author(s):  
M. Duraipandian ◽  
R. Vinothkanna
Keyword(s):  
Opinion Mining ◽  
Online Reviews ◽  
Classification Model ◽  
Product Reviews ◽  
Consumer Evaluations ◽  
Proposed Model ◽  
Svm Model ◽  
Review Helpfulness ◽  
Hybrid Classification ◽  
Corporate Trust

Customers post online product reviews based on their own experience. They may share their thoughts and comments on items on online shopping websites. The sentiment analysis comprises of opinion or idea process and process of sorting high rating reviews according to how well the product satisfies. Opinion mining is a technique for extracting useful data from large amounts of texts in order to use those to enhance or expand a company's operations. According to consumer evaluations, many of the goods aren't as good as they seem. It's common that buyers submit their thoughts on a product but then forget to rate it. The prior data preprocessing is more efficient to extract the features by CNN approach. This proposed methodology breaks down each user's rating prediction model into two parts: one based on the review text and other based on the user rating matrix with the help of CNN feature engineering. The goal of this study is to classify all reviews into ratings by SVM model. This proposed classification model provides good accuracy to predict the online reviews efficiently. For reviews without ratings, a further prediction of feelings is generated using multiple classifiers. The benefits of this proposed model are honed using helpfulness ratings from a small number of evaluations such as accuracy, F1 score, sensitivity, and precision. According to studies using the standard benchmark dataset, the accuracy of customized recommendation services, user happiness, and corporate trust may all be enhanced by including review helpfulness information in the recommender system.

Identifying the Opinion Orientation of Online Product Reviews at Feature Level

2017 ◽  
Vol 8 (2) ◽  
pp. 92-111
Author(s):  
Nilanshi Chauhan ◽  
Pardeep Singh
Keyword(s):  
Sentiment Analysis ◽  
Present Article ◽  
Online Shopping ◽  
Service Providers ◽  
Online Reviews ◽  
Product Reviews ◽  
Striking Increase ◽  
Feature Based ◽  
Potential Customers ◽  
Pruning Methods

This article describes how e-commerce has become so vast that almost every product and service can be purchased online, to be delivered at our doorsteps. This has led to a striking increase in the number of online customers. In an attempt to make the online shopping more appealing and transparent to the online customers, the e-retailers allow their customers to express their opinion about the purchased products and services. Recently, analysis of such online reviews has become an active topic of research. This is because it is of immense concern to various stakeholders vs. online merchants, potential customers and the manufacturers of the particular product or service providers. The present article addresses the problem of summarization of such opinions expressed online and aims to create an organized feature-based summary as a solution. The proposed system depends on the frequency of occurrences of the potential features. A number of pruning methods are applied in order to obtain the final feature set and sentiment analysis has been done for each such feature.

Survey on Review Spam of E-Commerce Sites

2014 ◽  
Vol 631-632 ◽  
pp. 1190-1193
Author(s):  
Sheng Xiu Yang ◽  
Lu Jie Fan
Keyword(s):  
Online Shopping ◽  
Important Determinant ◽  
Spam Detection ◽  
Product Reviews ◽  
Product Review ◽  
Review Spam ◽  
Quality Of Products ◽  
Fake Reviews ◽  
Potential Customers

Online shopping reviews provide valuable information for customers to compare the quality of products, and many other aspects of future purchases. People increasingly rely on information from E-commerce reviews. Product reviews is an important determinant of potential customers’ buying choices. However, spammers are joining this community to try to mislead consumers by writing fake or unfair reviews to confuse the consumers. Fake product review detection makes an attempt to detect fake reviews and remove them to restore the truthful ones for readers. To the best of our knowledge, there is still less published study on this problem. In this paper, we make a survey and an attempt to give a brief overview on review spam. The related work of fake product review detection is presented including web spam and spam email. Then some methods to detect review spam are introduced and summarized. The trend of review spam detection is concluded finally.

Topic modelling and opinion mining of user generated content on the internet using machine learning: An analysis of postpartum care centres in shanghai

2021 ◽  
pp. 1-8
Author(s):  
Susan (Sixue) Jia ◽  
Banggang Wu
Keyword(s):  
Machine Learning ◽  
Opinion Mining ◽  
Online Reviews ◽  
Postpartum Care ◽  
The Internet ◽  
Care Centre ◽  
Team Environment ◽  
The Status ◽  
Support Team ◽  
Postpartum Recovery

In order to reach a compromise between adhering to the traditional culture and embracing the modern lifestyle, more and more Asian moms are heading towards postpartum care centres for postpartum recovery. However, research regarding the quality of care of these postpartum care centres is nearly missing from the literature. This paper investigated the status quo of the postpartum care centres in Shanghai, China from mothers’ perspectives by means of analysing the 34280 pairs of ratings and reviews posted by postpartum care centre customers on the internet with machine learning and text mining. Results show that the mothers are generally satisfied with the studied care centres. Meanwhile, the 13 major topics in the customer online reviews were identified, which provide an overview of the interaction between a mother and a care centre. In addition, weight of topic analysis suggests that the studied care centres can further improve in the areas of support team, environment, and facility.

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