The Usage of Social Media Text Data for the Demand Forecasting in the Fashion Industry

Beheshti-Kashi, Samaneh; Thoben, Klaus‐Dieter

doi:10.1007/978-3-319-23512-7_72

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…Nonparametric learning models are those in which the number of parameters increases as the amount of training data increases. These models are more versatile, although they are computationally intensive [23][24][25][26]. The idea behind K-nearest neighbors computation in grouping is to identify a name by selecting a preset number of arranging tests that are closest in distance to the new point.…”

Section: K-nearest Neighborsmentioning

confidence: 99%

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Wang

2022

Wireless Communications and Mobile Computing

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Attributable to challenges of social event enormous volumes of material area information in a setting of less mining research, foreseeing the qualities of articles of clothing turns into a significant open issue which gets increasingly more consideration from the materials research local area. In this exploration work, the field of data mining endeavors to anticipate clothing insurance factors completely plan on understanding the computational individual of learning. Qualities of dress learning are being explored as a methodology for settling on the decision and utilization of getting ready data and their outcomes. It is seen from the results got by experimentation that the linear regression hushes up engaging as a result of adequacy as far as high forecast rate and linear regression can find the dress protection execution in a most proficient way in contrast with any remaining inclining calculations tested.

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Section: K-nearest Neighborsmentioning

confidence: 99%

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Wang

2022

Wireless Communications and Mobile Computing

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“…Recently, researchers have actively conducted text mining research in the fashion industry. Beheshti-Kashi and Toben (2016) [42] applied text mining to social media data to analyze fashion trends and categorize design features. Rizun and Kucharska (2018) [43] examined customers' interactions with fashion brands using a set of text mining algorithms.…”

Section: Big Data Analysismentioning

confidence: 99%

Exploring the Key Factors that Lead to Intentions to Use AI Fashion Curation Services through Big Data Analysis

2022

KSII TIIS

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An increasing number of companies in the fashion industry are using AI curation services. The purpose of this study is to investigate perceptions of and intentions to use AI fashion curation services among customers by using text mining. To accomplish this goal, we collected a total of 34,190 online posts from two Korean portals, Naver and Daum. We conducted frequency analysis to identify the most frequently mentioned keywords using Textom. The analysis extracted "various," "good," "many," "right," and "new" at the highest frequency, indicating that consumers had positive perceptions of AI fashion curation services. In addition, we conducted a semantic network analysis with the top-50 most frequently used keywords, classifying customers' perceptions of AI fashion curation services into three groups: shopping, platform, and business profit. We also identified the factors that boost continuous use intentions: usability, usefulness, reliability, enjoyment, and personalization. We conclude this paper by discussing the theoretical and practical implications of these findings.

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“…18 found that appearance affects consumers’ purchases more than product quality. As many people purchase their clothes online these days, media channels (such as Twitter 19 and Amazon 20 ) have also had a considerable impact on retailing strategy. Although external factors also have a certain impact on clothing retail, the extent of the effect is difficult to measure.…”

Section: Related Workmentioning

confidence: 99%

Sportswear retailing forecast model based on the combination of multi-layer perceptron and convolutional neural network

Zhou

Zhang

et al. 2021

Textile Research Journal

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Apparel sales forecasting plays an important role in production planning, distribution decision, and inventory management of enterprises. Especially, the sportswear market has been shown rapid growth characterized by long-term sales. This paper proposes a sales forecasting model for sportswear sales based on the multi-layer perceptron (MLP) and the convolutional neural network (CNN). A novel loss function is also proposed to improve the prediction accuracy. The proposed model is trained and validated on the time-series retailing data collected from three offline local sports stores in China. The influencing factors of retailing forecasting, such as time-series sales data, product features, distribution strategy, shop size, and other parameters, were also defined. Experimental results show that the proposed forecasting model outperforms the compared statistical methods by a large margin. Specifically, the proposed model provided 65% prediction accuracy, while the compared methods provided 16% prediction accuracy. The results show that the proposed model could be potentially used in sportswear sales forecasting, especially offline clothing and other long lifecycle clothing fields.

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The Usage of Social Media Text Data for the Demand Forecasting in the Fashion Industry

Cited by 11 publications

References 13 publications

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Exploring the Key Factors that Lead to Intentions to Use AI Fashion Curation Services through Big Data Analysis

Sportswear retailing forecast model based on the combination of multi-layer perceptron and convolutional neural network

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