International Journal of Computational Intelligence Systems

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Volume 12, Issue 2, 2019, Pages 1423 - 1435

Exploitation of Social Network Data for Forecasting Garment Sales

Authors
Chandadevi Giri1, 2, 3, 4, *, Sebastien Thomassey1, Xianyi Zeng1
1Laboratoire de Génie et Matériaux Textiles (GEMTEX), ENSAIT, F-59000 Lille, France
2The Swedish School of Textiles, University of Boras, S-50190 Boras, Sweden
3College of Textile and Clothing Engineering, Soochow University, Suzhou 215168, China
4Automatique, Génie informatique, Traitement du Signal et des Images, Université Lille Nord de France, F-59000 Lille, France
*Corresponding author. Email: chandadevi.giri@ensait.fr
Corresponding Author
Chandadevi Giri
Received 24 April 2019, Accepted 26 September 2019, Available Online 21 November 2019.
DOI
10.2991/ijcis.d.191109.001How to use a DOI?
Keywords
Social Media Data; Forecasting; Naïve Bayes; Sentiment analysis; Fuzzy forecasting model
Abstract

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.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
1423 - 1435
Publication Date
2019/11/21
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.191109.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Chandadevi Giri
AU  - Sebastien Thomassey
AU  - Xianyi Zeng
PY  - 2019
DA  - 2019/11/21
TI  - Exploitation of Social Network Data for Forecasting Garment Sales
JO  - International Journal of Computational Intelligence Systems
SP  - 1423
EP  - 1435
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.191109.001
DO  - 10.2991/ijcis.d.191109.001
ID  - Giri2019
ER  -