Mining customer requirements from online reviews: A product improvement perspective

Qi, Jiayin; Zhang, Zhenping; Jeon, Seongmin; Zhou, Yanquan

doi:10.1016/j.im.2016.06.002

Cited by 245 publications

(148 citation statements)

References 35 publications

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“…Online reviews can be the importance source of innovative ideas, providing input for new product designs and enhancements [30]. Therefore, significant number of studies has employed customer reviews as an important source for understanding customer requirements and feedbacks [30][31][32].…”

Section: Customer Review As An Excellent Source In Internet Commercementioning

confidence: 99%

“…Therefore, significant number of studies has employed customer reviews as an important source for understanding customer requirements and feedbacks [30][31][32]. Qi et al [30] developed an automatic filtering model to predict the helpfulness of online reviews. They employed a Kano model to the mapping rules for identifying qualified customer reviews.…”

Section: Customer Review As An Excellent Source In Internet Commercementioning

confidence: 99%

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Analyzing Dynamic Change in Customer Requirements: An Approach Using Review-Based Kano Analysis

2018

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Abstract:To seek sustainable product development, understanding customer requirements is critically important where the life cycle of products or services is so fast, and continuous updates should be provided. In particular, how a customer feels for the specific function of the product/service and how their needs have changed is a critical question. According to Kano model dynamics, customer requirements for certain functions change over time, because customers firstly feel attracted to the new service characteristics but come to take them for granted over time. However, previous research on proving this theory has relied on customer surveys and interviews, which are highly time-consuming and expensive. In response, this study suggests customer review-based analysis to investigate Kano model dynamics, because customer reviews can be considered to be excellent sources for reflecting customer needs. This study firstly categorizes customer reviews into two types-positive reviews and supplementation-required reviews-and suggests a five-section framework according to the frequency of each review type. We define characteristics of each section from the perspective of the Kano model. Based on this framework, we analyze the dynamics of customer requirements in the online businesses, for which customer reviews are the main indicator of service quality.

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Section: Customer Review As An Excellent Source In Internet Commercementioning

confidence: 99%

Section: Customer Review As An Excellent Source In Internet Commercementioning

confidence: 99%

Analyzing Dynamic Change in Customer Requirements: An Approach Using Review-Based Kano Analysis

2018

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“…The basis for sentiment analysis in product reviews lies in the accurate extraction of attribute‐opinion word pairs. Hence, some scholars have developed some consumer purchase decision methods on the basis of opinion pairs mining, which calculated the proportion or total quantity of positive and negative sentiments for the products to support the customer decisions 7,8 . However, comment information with neutral attitude of attribute‐opinion pairs was ignored in these studies, resulting in the loss of a great deal of decision‐making information.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, some scholars have developed some consumer purchase decision methods on the basis of opinion pairs mining, which calculated the proportion or total quantity of positive and negative sentiments for the products to support the customer decisions. 7,8 However, comment information with neutral attitude of attribute-opinion pairs was ignored in these studies, resulting in the loss of a great deal of decision-making information. For that, some studies took the neutral sentiments into account, and calculated the sentiment orientation of multidimensional attributes of products based on the product attribute classification framework of the website to support the online shopping.…”

mentioning

confidence: 99%

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

Yang

Ouyang

et al. 2020

Int J Intell Syst

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In the process of online shopping, consumers usually compare the review information of the same product in different e‐commerce platforms. The sentiment orientation of online reviews from different platforms interactively influences on consumers’ purchase decision. However, due to the limitation of the ability to process information manually, it is difficult for a consumer to accurately identify the sentiment orientation of all reviews one by one and describe the process of their interactive influence. To this end, we proposed an online shopping support model using deep‐learning–based opinion mining and q‐rung orthopair fuzzy interaction weighted Heronian mean (q‐ROFIWHM) operators. First, in the proposed method, the deep‐learning model is used to automatically extract different product attribute words and opinion words from online reviews, and match the corresponding attribute‐opinion pairs; meanwhile, the sentiment dictionary is used to calculate sentiment orientation, including positive, negative, and neutral sentiments. Second, the proportions of the three kinds of sentiments about each attribute of the same product are calculated. According to the proportion value of attribute sentiment from different platforms, the sentiment information is converted into multiple cross‐decision matrices, which are represented by the q‐rung orthopair fuzzy set. Third, considering the interactive characteristics of decision matrix, the q‐ROFIWHM operators are proposed to aggregate this cross‐decision information, and then the ranking result was determined by score function to support consumers' purchase decisions. Finally, an actual example of mobile phone purchase is given to verify the rationality of the proposed method, and the sensitivity and the comparison analysis are used to show its effectiveness and superiority.

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“…In order to enhance the customer satisfaction associated with the shopping experience, almost every e-commerce transection platform has designed the function of user evaluation. These reviews are termed as quite critical for the consumers, businesses, and manufacturers [1], since they not only impact the consumers' shopping decisions [2,3] or word-of-mouth intention [4] and merchants' purchasing strategies but also impact the design and improvement of the products [1,5]. Therefore, consumer reviews are a substantial information resource [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

2018

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How to acquire useful information intelligently in the age of information explosion has become an important issue. In this context, sentiment analysis emerges with the growth of the need of information extraction. One of the most important tasks of sentiment analysis is feature extraction of entities in consumer reviews. This paper first constitutes a directed bipartite feature-sentiment relation network with a set of candidate features-sentiment pairs that is extracted by dependency syntax analysis from consumer reviews. Then, a novel method called MHITS which combines PMI with weighted HITS algorithm is proposed to rank these candidate product features to find out real product features. Empirical experiments indicate the effectiveness of our approach across different kinds and various data sizes of product. In addition, the effect of the proposed algorithm is not the same for the corpus with different proportions of the word pair that includes the "bad", "good", "poor", "pretty good", "not bad" these general collocation words.

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Mining customer requirements from online reviews: A product improvement perspective

Cited by 245 publications

References 35 publications

Analyzing Dynamic Change in Customer Requirements: An Approach Using Review-Based Kano Analysis

Analyzing Dynamic Change in Customer Requirements: An Approach Using Review-Based Kano Analysis

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

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