Extraction of affective responses from customer reviews: an opinion mining and machine learning approach

Li, Zhi; Tian, Zonggui; Wang, J. W.; Wang, Wai Ming

doi:10.1080/0951192x.2019.1571240

Cited by 34 publications

(13 citation statements)

References 41 publications

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“…Compared with the existing research on product design improvement based on review mining, the proposed method has also made some progress. Existing research usually analyze only the positive or negative opinions of users about a product feature [32,33], but since a product feature has many attributes, such a general opinion is of little value to guiding product design improvement. Benefiting from the guidance of UX theory and UX classical evaluation methods, the proposed method introduces UX aspect into the analysis of review data.…”

Section: Discussionmentioning

confidence: 99%

“…Qi et al [31] performed attribute identification and sentiment analysis, and utilized the KANO model to analyze the data to develop appropriate product improvement strategies. Li et al [32] proposed a sentiment analysis approach based on Kansei Engineering and machine learning to extract and measure users' affective responses to products from online reviews. Jin et al [33] extracted aspects of product features and detailed reasons of consumer dissatisfaction to inform designers regarding what leads to unsatisfied opinions.…”

Section: Mining Online Product Reviewsmentioning

confidence: 99%

“…(4) Degree term extraction. The degree terms in the reviews are extracted by using the available degree word lexicon [32].…”

Section: Extraction Of Key Elementsmentioning

confidence: 99%

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User experience evaluation method based on online product reviews

Cheng

Qin

et al. 2021

IFS

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Mining Online Product Reviewsmentioning

confidence: 99%

See 1 more Smart Citation

User experience evaluation method based on online product reviews

Cheng

Qin

et al. 2021

IFS

View full text Add to dashboard Cite

show abstract

“…Online reviews available on e-commerce web site and social media can solve the above-said problem [15]. Customer reviews available on these sites are increasing at a very fast pace due to rapid Internet growth [2]. Therefore, sentiment analysis is extensively used to extract and evaluate customer opinions and sentiments from these online reviews in various fields [16], [25], [26].…”

Section: B Sentiment Analysismentioning

confidence: 99%

“…The technique to integrate the customer's feedback into product and service design is known as 'Kansei engineering' [1]. The Kansei engineering aims to develop a relationship between customer's sentiments and product design parameters [2]. This will help the product to achieve maximum customer satisfaction, which in turn uplifts the company's business in the market [3].…”

Section: Introductionmentioning

confidence: 99%

Aspect Based Sentiment Analysis of Ridesharing Platform Reviews for Kansei Engineering

2020

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At present online reviews are becoming an important source for Kansei engineering of the services provided by ridesharing platforms. Kansei engineering deals with incorporating customer feedback and demands into product and service design. Thus, it is used as a tool for organizations to uplift their businesses by considering customer reviews and feedback. Customer reviews available on social media are in unstructured form; therefore, sentiment analysis is employed to extract customer's opinions in a systematic manner. In India-Pakistan, these reviews are mostly in Roman Urdu/Hindi and English, which are of great value for ridesharing platforms as a part of their Kansei engineering strategy. However, sentiment analysis cannot be performed directly on these reviews as they are mostly in Roman Urdu/Hindi. Therefore, the objective of this paper is to conduct aspect based sentiment analysis on these reviews after translating them into English for Kansei engineering of the service. Consequently, sentiment analysis is carried out to extract the most frequent features along with nouns and adjectives used by the customers to express their sentiments. We extracted prominent aspects of the service (i.e., 'Driver', 'Company', 'Service', and 'Ride') based on their highest frequencies using aspect based sentiment analysis. The customer sentiments are then clustered into these main aspects using unsupervised machine learning technique. Each aspect is further analyzed based on their polarity, which serves as an input for Kansei engineering of the service. As a result, it can facilitate ridesharing companies to enhance their businesses by improving services in accordance with customer demands.

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Analyzing User Reviews on Digital Detox Apps: A Text Mining and Sentiment Analysis Approach

Khan,

Khan

2024

J of Consumer Behaviour

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Due to the growing concerns around problematic smartphone use and its negative impact, there is a rising interest in digital detox. While many digital detox apps have been developed in recent years, there is still limited understanding of the long‐term effectiveness of digital detox applications and the attitude of people towards these apps. This study fills this gap by identifying the topics that people post in their reviews on the Google Play Store about digital detox apps and the emotion‐based sentiment of those reviews. A total of 3500 reviews of 25 digital detox apps were collected from the Google Play Store using a scraping tool called “Parsehub.” Data was analyzed using R studio. Sentiment analysis results suggest that positive sentiments dominated the data frame. “Trust” and “anticipation” were the two most expressed emotions in the reviews. Regression analysis confirmed that sentiment scores could explain the ratings of the apps. Through LDA topic modeling four major topics of the reviews were identified and are discussed in detail in the later section of the research paper. The findings of this study may help app developers and marketers improve digital detox apps so that people can learn and practice mindful smartphone use with the help of these apps. This study fills a gap in digital detox research by adopting a new methodological approach and procedure since it combines text mining, sentiment analysis (NRC Lexicon using Syuzhet package), regression analysis, and LDA topic modeling. To the best of our knowledge, this is the first study which uses this research approach in the context of digital detox apps.

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Extraction of affective responses from customer reviews: an opinion mining and machine learning approach

Cited by 34 publications

References 41 publications

User experience evaluation method based on online product reviews

User experience evaluation method based on online product reviews

Aspect Based Sentiment Analysis of Ridesharing Platform Reviews for Kansei Engineering

Analyzing User Reviews on Digital Detox Apps: A Text Mining and Sentiment Analysis Approach

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