Exploitation of Social Network Data for Forecasting Garment Sales
- 10.2991/ijcis.d.191109.001How to use a DOI?
- Social Media Data; Forecasting; Naïve Bayes; Sentiment analysis; Fuzzy forecasting model
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.
- © 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
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 -