Exploitation of Social Network Data for Forecasting Garment Sales

Giri, Chandadevi; Thomassey, Sebastien; Zeng, Xianyi

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.2991/ijcis.d.191109.001

URL permanente :

http://hdl.handle.net/20.500.12210/84867

Titre :

Exploitation of Social Network Data for Forecasting Garment Sales

Auteur(s) :

Giri, Chandadevi [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Thomassey, Sebastien [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Zeng, Xianyi [Auteur]
Génie et Matériaux Textiles [GEMTEX]

Titre de la revue :

International Journal of Computational Intelligence Systems

Nom court de la revue :

Int. J. Comput. Intell. Syst.

Numéro :

Pagination :

1423-1435

Éditeur :

Atlantis Press

Date de publication :

2019-11-21

ISSN :

1875-6891

Mot(s)-clé(s) en anglais :

Social Media Data
Forecasting
Naive Bayes
Sentiment analysis
Fuzzy forecasting model

Discipline(s) HAL :

Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Growing use of social media such as Twitter, Instagram, Facebook, etc., by consumers leads to the vast repository of consumer generated data. Collecting and exploiting these data has been a great challenge for clothing ...
Lire la suite >Growing use of social media such as Twitter, Instagram, Facebook, etc., by consumers leads to the vast repository of consumer generated data. Collecting and exploiting these data has been a great challenge for clothing industry. This paper aims to study the impact of Twitter on garment sales. In this direction, we have collected tweets and sales data for one of the popular apparel brands for 6 months from April 2018 – September 2018. Lexicon Approach was used to classify Tweets by sentence using Naïve Bayes model applying enhanced version of Lexicon dictionary. Sentiments were extracted from consumer tweets, which was used to map the uncertainty in forecasting model. The results from this study indicate that there is a correlation between the apparel sales and consumer tweets for an apparel brand. “Social Media Based Forecasting (SMBF)” is designed which is a fuzzy time series forecasting model to forecast sales using historical sales data and social media data. SMBF was evaluated and its performance was compared with Exponential Forecasting (EF) model. SMBF model outperforms the EF model. The result from this study demonstrated that social media data helps to improve the forecasting of garment sales and this model could be easily integrated to any time series forecasting model.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

Génie des matériaux textiles (GEMTEX) - ULR 2641

Date de dépôt :

2023-06-20T11:26:23Z
2024-03-18T11:21:52Z

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Ce document est diffusé sous licence Attribution-NonCommercial 3.0 United States

Numéro de version	Lien	Date de modification
2	20.500.12210/84867*	2024-03-18T11:18:31Z
1	20.500.12210/84867.1	2023-06-20T11:26:23Z

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