A Machine Learning Approach to Analyze Fashion Styles from Large Collections of Online Customer Reviews

Hananto, Valentinus Roby; Kim, Soomin; Kovacs, Mate; Serdült, Uwe; Kryssanov, Victor V.

doi:10.1109/icbir52339.2021.9465830

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…Not many studies were conducted on product reviews. Majority studies have taken Amazon [49], [50], [69]- [72] or Rakuten Ichiba (Japan) marketplace [69] as their source of data. Rakuten Ichiba give more variety in sportswear and cloth products compared to Amazon [69].…”

Section: G Comparison With Competitor Productsmentioning

confidence: 99%

Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining

Abdul Rahman,

A. Talip,

Sarirah

2024

IJPCC

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Online consumer reviews in e-commerce are one technique to gather consumer opinion and sentiment about a company's products and services. However, manual analysis is impractical due to natural language text's enormous volume and complexity. Text mining and sentiment analysis methods based on machine learning provide an opportunity to analyze data for marketing objectives by increasing sales, positive electronic word-of-mouth (e-WOM), and meeting consumer demands and wants through the enhancement of market offerings. Despite the numerous benefits of analyzing e-commerce reviews to assist a company's marketing strategy, very little research has focused on sentiment and acceptance for Malaysia’s local agriculture products due to mixed language (English-Malay language) processing challenges. This concept paper highlights the use of text mining techniques to extract valuable insights from e-commerce comments related to Malaysian local agriculture products. By leveraging text mining, the study aims to better understand consumer sentiments, preferences, and feedback regarding local products, thereby facilitating improved market analysis and decision-making processes.

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Section: G Comparison With Competitor Productsmentioning

confidence: 99%

Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining

Abdul Rahman,

A. Talip,

Sarirah

2024

IJPCC

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“…In order to transition the traditional buying experience to e-commerce, NLP is mostly utilized for fashion advice and recommendations [34,78,79,124,126,[155][156][157][158][159][160][161][162][163][164][165][166][167][168]. Another significant area of study focuses on gathering data from consumers using sentiment analysis techniques, such as via reviews or social media [42,44,[169][170][171][172][173][174][175][176][177][178][179][180].…”

Section: Ai In B2c Retail and Application Areas Of The Techniquesmentioning

confidence: 99%

“…Collectedfromreviewsofretailshops [42,44,160,165,169,170,174,[178][179][180]183] Collectedfromscanner [46,132] Collectedfromsocialmedia [107,116,172,175,222] CollectedfromtheInternet [81,95,102,104,117,128,153,155,167,168,176,177,192,203,225,…”

Section: Databases Citationsmentioning

confidence: 99%

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

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Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research.

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“…Anh et al [33] presented how to extract useful comments from customers and the implications for the early stages of product design and proposed a framework for analyzing comments on online shopping sites and it can automatically extract useful information from review documents and it can also collaboratively work between designers and opinion customers. Hananto et al [34] developed a fashion style model to investigate fashion styles by using online customer reviews to analyze customer preferences. The obtained fashion style models can potentially help marketing and product design specialists better understand customer preferences in the ecommerce fashion industry.…”

Section: Analysis Of Customer Requirementsmentioning

confidence: 99%

A dynamic customer requirement mining method for continuous product improvement

Zhao

Guo

et al. 2022

Auton. Intell. Syst.

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The key to successful product development is better understanding of customer requirements and efficiently identifying the product attributes. In recent years, a growing number of researchers have studied the mining of customer requirements and preferences from online reviews. However, since customer requirements often change dynamically on multi-generation products, most existing studies failed to discover the correlations between customer satisfaction and continuous product improvement. In this work, we propose a novel dynamic customer requirement mining method to analyze the dynamic changes of customer satisfaction of product attributes based on sentiment and attention expressed in online reviews, aiming to better meet customer requirements and provide the direction and content of future product improvement. Specifically, this method is divided into three parts. Firstly, text mining is adopted to collect online review data of multi-generation products and identify product attributes. Secondly, the attention and sentiment scores of product attributes are calculated with a natural language processing tool, and further integrated into the corresponding satisfaction scores. Finally, the improvement direction for next-generation products is determined based on the changing satisfaction scores of multi-generation product attributes. In addition, a case study on multi-generation phone products based on online reviews was conducted to illustrate the effectiveness and practicality of the proposed methodology. Our research completes the field of requirements analysis and provides a new dynamic approach to requirements analysis for continuous improvement of multi-generation products, which can help enterprises to accurately understand customer requirements and improve the effectiveness and efficiency of continuous product improvement.

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A Machine Learning Approach to Analyze Fashion Styles from Large Collections of Online Customer Reviews

Cited by 8 publications

References 14 publications

Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining

Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

A dynamic customer requirement mining method for continuous product improvement

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