Abstract Argumentation for Summarizing Product Reviews: A Case Study in Shopee Thailand

2019 ◽  
Author(s):  
Teeradaj Racharak
Keyword(s):  
Product Reviews ◽  
Abstract Argumentation

Ontology-Based Opinion Mining for Online Product Reviews

2019 ◽  
pp. 123-148 ◽  
Author(s):  
Farheen Siddiqui ◽  
Parul Agarwal
Keyword(s):  
Information Extraction ◽  
Opinion Mining ◽  
Domain Ontology ◽  
Literature Survey ◽  
Extensive Literature ◽  
Product Reviews ◽  
Product Domain ◽  
User Centric ◽  
Selection Of

In this chapter, the authors work at the feature level opinion mining and make a user-centric selection of each feature. Then they preprocess the data using techniques like sentence splitting, stemming, and many more. Ontology plays an important role in annotating documents with metadata, improving the performance of information extraction and reasoning, and making data interoperable between different applications. In order to build ontology in the method, the authors use (product) domain ontology, ConceptNet, and word net databases. They discuss the current approaches being used for the same by an extensive literature survey. In addition, an approach used for ontology-based mining is proposed and exploited using a product as a case study. This is supported by implementation. The chapter concludes with results and discussion.

Extracting Consumer Preference From User-Generated Content Sources Using Classification

2013 ◽  
Author(s):  
Thomas Stone ◽  
Seung-Kyum Choi
Keyword(s):  
Consumer Preference ◽  
Data Interpretation ◽  
User Generated Content ◽  
Support Vector ◽  
Product Reviews ◽  
Preference Modeling ◽  
Opinion Extraction ◽  
Design Case Study

The use of online, user-generated content for consumer preference modeling has been a recent topic of interest among the engineering and marketing communities. With the rapid growth of many different types of user-generate content sources, the tasks of reliable opinion extraction and data interpretation are critical challenges. This research investigates one of the largest and most-active content sources, Twitter, and its viability as a content source for preference modeling. Support Vector Machine (SVM) is used for sentiment classification of the messages, and a Twitter query strategy is developed to categorize messages according to product attributes and attribute levels. Over 7,000 messages are collected for a smartphone design case study. The preference modeling results are compared with those from a typical product review study, including over 2,500 product reviews. Overall, the results demonstrate that consumers do express their product opinions through Twitter; thus, this content source could potentially facilitate product design and decision-making via preference modeling.

scholarly journals Reasoning with Inconsistent Possibilistic Ontologies by Applying Argument Accrual

2017 ◽  
Vol 17 (02) ◽  
pp. e16
Author(s):  
Sergio Alejandro Gómez
Keyword(s):  
Logic Programming ◽  
Description Logic ◽  
Logic Programs ◽  
Argumentation Framework ◽  
Abstract Argumentation ◽  
Possibilistic Logic ◽  
Grounded Semantics

We present an approach for performing instance checking in possibilistic description logic programming ontologies by accruing arguments that support the membership of individuals to concepts. Ontologies are interpreted as possibilistic logic programs where accruals of arguments as regarded as vertexes in an abstract argumentation framework. A suitable attack relation between accruals is defined. We present a reasoning framework with a case study and a Java-based implementation for enacting the proposed approach that is capable of reasoning under Dung’s grounded semantics.

Visualization Tool for Interpreting User Needs From User-Generated Content via Text Mining and Classification

2014 ◽  
Author(s):  
Thomas Stone ◽  
Seung-Kyum Choi
Keyword(s):  
Language Processing ◽  
Consumer Products ◽  
Machine Learning Algorithms ◽  
User Generated Content ◽  
Visualization Tool ◽  
Product Reviews ◽  
Social Media Platforms ◽  
Processing Techniques ◽  
Promising Source

The amount of user-generated content related to consumer products continues to grow as users increasingly take advantage of forums, product review sites, and social media platforms. The content is a promising source of insight into users’ needs and experiences. However, the challenge remains as to how concise and useful insights can be extracted from large quantities of unstructured data. We propose a visualization tool which allows designers to quickly and intuitively sift through large amounts of user-generated content and derive useful insights regarding users’ perceptions of product features. The tool leverages machine learning algorithms to automate labor-intensive portions of the process, and no manual labeling is required by the designer. Language processing techniques are arranged in a novel way to guide the designer in selecting the appropriate inputs, and multidimensional scaling enables presentation of the results in concise 2D plots. To demonstrate the efficacy of the tool, a case study is performed on action cameras. Product reviews from Amazon.com are analyzed as the user-generated content. Results from the case study show that the tool is helpful in condensing large amounts of user-generated content into useful insights, such as the key differentiations that users perceive among similar products.

scholarly journals User experience evaluation method based on online product reviews

2021 ◽  
pp. 1-15
Author(s):  
Fangmin Cheng ◽  
Suihuai Yu ◽  
Shengfeng Qin ◽  
Jianjie Chu ◽  
Jian Chen
Keyword(s):  
New Product Development ◽  
User Experience ◽  
Evaluation Method ◽  
Information Source ◽  
New Product ◽  
Product Reviews ◽  
Vacuum Cleaner ◽  
Online Product Reviews

Evaluating the quality of the user experience (UX) of existing products is important for new product development. Conventional UX evaluation methods, such as questionnaire, have the disadvantages of the great subjective influence of investigators and limited number of participants. Meanwhile, online product reviews on e-commerce platforms express user evaluations of product UX. Because the reviews objectively reflect the user opinions and contain a large amount of data, they have potential as an information source for UX evaluation. In this context, this study explores how to evaluate product UX through using online product reviews. A pilot study is conducted to define the key elements of a review. Then, a systematic method of product UX evaluation based on reviews is proposed. The method includes three parts: extraction of key elements, integration of key elements, and quantitative evaluation based on rough number. The effectiveness of the proposed method is demonstrated by a case study using reviews of a wireless vacuum cleaner. Based on the proposed method, designers can objectively evaluate the UX quality of existing products and obtain detailed suggestions for product improvement.

The Effect of Online Q&As and Product Reviews on Product Performance Metrics: Amazon.com as a Case Study

2021 ◽  
Vol 20 (01) ◽  
pp. 2150005
Author(s):  
Reza Mousavi ◽  
Bidyut Hazarika ◽  
Kuanchin Chen ◽  
Muhammad Razi
Keyword(s):  
Performance Metrics ◽  
Online Reviews ◽  
Review Literature ◽  
Product Performance ◽  
Product Reviews ◽  
Product Sale ◽  
Comparison Shopping ◽  
Questions And Answers ◽  
Review Helpfulness

Online reviews have received an overwhelming interest in the recent decades. Comparatively speaking, the online product questions and answers (Q&As) have received less attention than online reviews, despite that they both affect the image and the value of a project. Although online reviews and Q&As are both forms of user generated knowledge ion online communities, they may affect customers decision making differently. Furthermore, Q&As are very useful for pre-purchase information-searching and comparison shopping, especially when online product reviews either do not provide the needed answer or getting the desired information requires additional “cost” (i.e. time and effort) to sort out. Our findings show that Q&A traits had a varying effect on the product performance. We also found that review helpfulness is another important factor that affects product sale and popularity on e-commerce sites. The present study adds to existing electronic word-of-mouth (eWOM) and product review literature.

scholarly journals A CROSS-PLATFORM MARKET STRUCTURE ANALYSIS METHOD USING ONLINE PRODUCT REVIEWS

2021 ◽  
Vol 27 (5) ◽  
pp. 992-1018
Author(s):  
Gang Kou ◽  
Pei Yang ◽  
Yi Peng ◽  
Hui Xiao ◽  
Feng Xiao ◽  
...  
Keyword(s):  
Decision Making ◽  
Market Structure ◽  
Structure Analysis ◽  
Group Decision ◽  
Product Reviews ◽  
Analysis Method ◽  
Market Structure Analysis ◽  
Online Product Reviews ◽  
Cross Platform

Studies have shown that online product reviews can indicate the position of a competitive brand. Even though reviews on different platforms may express different opinions, most studies are based on only one platform. This may lead to an inaccurate analysis of market structure. To solve this problem, we develop a novel market structure analysis based on multi-attribute group decision-making which can integrate reviews from different platforms. Multiple platforms more comprehensively reflect the market than single platforms do. To verify the effectiveness of the proposed method, we conduct a case study of mobile phone reviews across three top e-commerce platforms in China. In addition, we propose a process to generate priorities for product-attribute improvements using a cross-platform market structure analysis method. Our experiments demonstrate the effectiveness of the proposed method.

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