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 Hubungan Karakteristik Individu terhadap Penilaian Kualitas Produk Apotek Rawat Jalan

2015 ◽  
Vol 3 (2) ◽  
pp. 108
Author(s):  
Ade Nurma Ruditya ◽  
Djazuly Chalidyanto
Keyword(s):  
Quality Assessment ◽  
Product Quality ◽  
Employment Status ◽  
Cross Sectional Study ◽  
Bivariate Analysis ◽  
Individual Factors ◽  
Design Data ◽  
Cross Sectional ◽  
Quality Dimensions ◽  
The Individual

ABSTRACTPharmacy services in hospitals need special attention to achieve effectiveness and efficiency of quality dimensions. Achievement of customer satisfaction figures in Dr. Moewardi Pharmacy Hospital Surakarta didn't meet hospital standards that set in the amount of ≥ 80% over the years 2013 to 2014, especially in the outpatient pharmacy. The purpose of this research is to study the relationship between the individual factors of patient (age, sex, educational level, employment status, income level) to product quality assessment in an outpatient pharmacy. This research is an observational analytic with cross sectional study design. Data was collected by interview using a questionnaire. Analyzed using univariate and bivariate analysis. The Results of the analysis of the factor that were significantly associated with the assessment of product quality are age and employment status. The majority of respondents give high scores for product quality assessment. Individual patient factors associated with product quality assessment in outpatient pharmacies are age and employment status. Need to maintain the quality of existing products, and specifically to the dimensions of the product's features need to increase in order to better product quality.Keywords: pharmacy services, individual factors, products quality

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.

Analysis on Opinion Words Extraction in Electronic Product Reviews

2019 ◽  
Vol 10 (1) ◽  
pp. 47-61
Author(s):  
Sint Sint Aung
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Research Area ◽  
Sentiment Classification ◽  
Training Data ◽  
Product Reviews ◽  
Research Areas ◽  
Product Features ◽  
Opinion Extraction ◽  
Training Examples

Online user reviews are increasingly becoming important for measuring the quality of different products and services. Sentiment classification or opinion mining involves studying and building a system that collects data from online and examines the opinions. Sentiment classification is also defined as opinion extraction as the computational research area of subjective information towards different products. Opinion mining or sentiment classification has attracted in many research areas because of its usefulness in natural language processing and other area of applications. Extracting opinion words and product features are also important tasks in opinion mining. In this work an unsupervised approach was proposed to extract opinions and product features without training examples. To obtain the dependency relation between the product aspects and opinions, this work used StanfordCoreNLP dependency parser. From these relations, rules are predified to extract product and opinions. The main advantage of this approach is that there is no need for training data and it has domain independence. Acoording to the experimental results, the modified algorithm gets better results than the double propagation algorithm.

Identifying the Relations Between Product Features and Sales Rank From Online Reviews

2016 ◽  
Author(s):  
Dedy Suryadi ◽  
Harrison Kim
Keyword(s):  
Regression Model ◽  
Online Reviews ◽  
Product Reviews ◽  
Independent Variables ◽  
Part Of Speech Tagging ◽  
Design Time ◽  
Part Of Speech ◽  
Product Features ◽  
Technology Products ◽  
Speech Tagging

Online product reviews have become an efficient source to gather consumer needs, instead of going through the labor-intensive surveys. The contribution of the paper is to relate the content of online reviews to a product’s sales rank, that implicitly reflects the needs and motivation behind what drives customers to purchase the product. In particular, the review content includes product features stated in the review, together with the sentiment expressed towards the feature. Part-of-speech tagging is used to extract the features and sentiment from the reviews. The extracted data from reviews and price then subsequently become independent variables in the regression model, while sales rank is the dependent variable. An experiment is run for the wearable technology products to illustrate the methodology and interpret the results. In general, the features in reviews that are related to sales rank significantly are button, calorie tracker, design, time functions, and waterproof abilities. Moreover, the products are further stratified based on price average. In the cluster of the most expensive items, the sales rank is found to be not significantly related to price.

Mining opinionated product features using WordNet lexicographer files

2016 ◽  
Vol 43 (6) ◽  
pp. 769-785 ◽  
Author(s):  
Saif A. Ahmad Alrababah ◽  
Keng Hoon Gan ◽  
Tien-Ping Tan
Keyword(s):  
Assessment Tool ◽  
Empirical Evaluation ◽  
Online Reviews ◽  
Product Reviews ◽  
Specific Domain ◽  
Customer Reviews ◽  
Product Features ◽  
Online Customer Reviews ◽  
Customer Perspective ◽  
Massive Number

Online customer reviews are an important assessment tool for businesses as they contain feedback that is valuable from the customer perspective. These reviews provide a significant basis on which potential customers can select the product that best meets their preferences. In online reviews, customers describe positive or negative experiences with a product or service or any part of it (i.e. features). Consumers frequently experience difficulty finding the desired product for comparison because of the massive number of online reviews. The automatic extraction of important product features is necessary to support customers in search of relevant product features. These features are the criteria that make it possible for customers to characterise different types of products. This article proposes a domain independent approach for identifying explicit opinionated features and attributes that are strongly related to a specific domain product using lexicographer files in WordNet. In our approach, N_gram analysis and the SentiStrength opinion lexicon have been employed to support the extraction of opinionated features. The empirical evaluation of the proposed system using online reviews of two popular datasets of supervised and unsupervised systems showed that our approach achieved competitive results for feature extraction from 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 Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

2020 ◽  
Vol 47 (2) ◽  
pp. 105-121
Author(s):  
Wie Wei ◽  
Yi-Ping Liu ◽  
Lei-Ru Wei
Keyword(s):  
Sentiment Analysis ◽  
Academic Research ◽  
Online Reviews ◽  
Domain Ontology ◽  
Business Strategies ◽  
Product Reviews ◽  
Analysis Framework ◽  
Fine Grained ◽  
Product Features ◽  
Sentiment Dictionary

Mining product reviews and sentiment analysis are of great significance, whether for academic research purposes or optimizing business strategies. We propose a feature-level sentiment analysis framework based on rules parsing and fine-grained domain ontology for Chinese reviews. Fine-grained ontology is used to describe synonymous expressions of product features, which are reflected in word changes in online reviews. First, a semiautomatic construction method is developed by using Word2Vec for fine-grained ontology. Then, feature-level sentiment analysis that combines rules parsing and the fine-grained domain ontology is conducted to extract explicit and implicit features from product reviews. Finally, the domain sentiment dictionary and context sentiment dictionary are established to identify sentiment polarities for the extracted feature-sentiment combinations. An experiment is conducted on the basis of product reviews crawled from Chinese e-commerce websites. The results demonstrate the effectiveness of our approach.

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.

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