What makes consumers unsatisfied with your products: Review analysis at a fine-grained level

Jin, Jian; Ji, Ping; Kwong, C. K.

doi:10.1016/j.engappai.2015.05.006

Cited by 51 publications

(22 citation statements)

References 28 publications

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“…They therefore proposed a method to translate sentences in social media text into a set of keywords representing customer preference, based on a co-word network generated from the review text. Jin, Ji, and Kwong (2016) found that sentiment analysis at the feature level failed to give enough information on reasons for dissatisfaction. They thus proposed a method to represent the detailed reasons stated in the reviews by a set of words.…”

Section: Other-entity-based Summarizationmentioning

confidence: 99%

Mining customer product reviews for product development: A summarization process

Hou

Yannou

Leroy

et al. 2019

Expert Systems with Applications

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This research set out to identify and structure from online reviews the words and expressions related to customers' likes and dislikes to guide product development. Previous methods were mainly focused on product features. However, reviewers express their preference not only on product features. In this paper, based on an extensive literature review in design science, the authors propose a summarization model containing multiples aspects of user preference, such as product affordances, emotions, usage conditions. Meanwhile, the linguistic patterns describing these aspects of preference are discovered and drafted as annotation guidelines. A case study demonstrates that with the proposed model and the annotation guidelines, human annotators can structure the online reviews with high inter-agreement. As high inter-agreement human annotation results are essential for automatizing the online review summarization process with the natural language processing, this study provides materials for the future study of automatization.

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Section: Other-entity-based Summarizationmentioning

confidence: 99%

Mining customer product reviews for product development: A summarization process

Hou

Yannou

Leroy

et al. 2019

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…They analyze the semantics and sentiments of sentences contained in user reviews to extract useful feedback and recommend to developers potential changes. Jin et al [47] study aspects of product features and detailed reasons which are extracted from online reviews to inform designers regarding what leads to unsatisfied opinions. Iacob and Harrison [48] present MARA, an automated system that extracts and analyzes app reviews to identify feature requests.…”

Section: Requirement and Feedback Miningmentioning

confidence: 99%

Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review

et al. 2019

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The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented.INDEX TERMS App marketing, user profiling, popularity prediction, app recommendation, usage pattern mining, mobile crowdsourcing.The associate editor coordinating the review of this manuscript and approving it for publication was Javier Medina.

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“…Customers/consumers are included in the key stakeholder groups of companies in all industries (B2B customers, B2C consumers) because their purchasing behavior and decisions (loyalty) determine the financial performance, capability and prospects of the company, and thus, the ability to meet the expectations of other groups (Zavyalova, In addition, customers/consumers are keen to share their comments on the goods and services they buy, the level of service, the handling of complaints and so on, in online forums (Jin, et al, 2016;Viglia, et al, 2016;Pfeffer, et al, 2014), as well as through the activity of various consumer organizations (demonstrations, boycotts, lawsuits) or other social organizations.…”

Section: Customers/consumers As Creators Of the Company's Reputationmentioning

confidence: 99%

Digital Customer as a Creator of the Reputation of Modern Companies

Szwajca

2019

Foundations of Management

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Scientific and technical developments and progressive digitization in almost all areas of life have led to the development of a new type of digital customer, with specific requirements and expectations for suppliers of goods and services. Customers, in particular consumers, are one of the key groups of stakeholders who create the corporate reputation. Customers are first of all creators of the corporate reputation as an “offeror” and “supplier” of value to buyers, as well as co-creators of the company’s reputation as a market player, employer, business partner or citizen, because their purchase decisions determine the company’s ability to meet the expectations of other stakeholder groups, that is, investors, employees or business partners. Building and maintaining a good reputation requires proper identification and satisfaction of the needs of key stakeholder groups. The aim of the article is to identify the specific requirements and expectations of digital customers in the context of building and maintaining a good reputation of the company. The thesis is that the digital customer expects, first and foremost, reliability and personalization in their relations with the supplier.

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What makes consumers unsatisfied with your products: Review analysis at a fine-grained level

Cited by 51 publications

References 28 publications

Mining customer product reviews for product development: A summarization process

Mining customer product reviews for product development: A summarization process

Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review

Digital Customer as a Creator of the Reputation of Modern Companies

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