Exploitation of Social Network Data for Forecasting Garment Sales

Giri, Chandadevi; Thomassey, Sebastien; Zeng, Xianyi

Document type :

Article dans une revue scientifique: Article original

DOI :

10.2991/ijcis.d.191109.001

Permalink :

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

Title :

Exploitation of Social Network Data for Forecasting Garment Sales

Author(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]

Journal title :

International Journal of Computational Intelligence Systems

Abbreviated title :

Int. J. Comput. Intell. Syst.

Volume number :

Pages :

1423-1435

Publisher :

Atlantis Press

Publication date :

2019-11-21

ISSN :

1875-6891

English keyword(s) :

Social Media Data
Forecasting
Naive Bayes
Sentiment analysis
Fuzzy forecasting model

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

Génie des matériaux textiles (GEMTEX) - ULR 2641

Submission date :

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

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial 3.0 United States

Version	Link	Modification date
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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