| dc.description.abstract | Demand forecasting has been a common topic for many years, especially after COVID 19, the search for solutions to uncertainty has been more focused than ever. Social media
has proven its place in other fields such as travel and entertainment when it comes to
forecasting, yet it seems to be overlooked by academia in the field of logistics.
This thesis aims to prove the promising potential of social-media-based data in
forecasting demand in the e-commerce field. The data included in this study is drawn
from a Vietnamese cosmetic company on the most well-known e-commerce platform
in Vietnam - Shopee. Datasets combine (1) baseline data which includes sales, prices,
rating, rating records, and discount programs; (2) social-media-based data which
includes information related to likes, comments and shares on Facebook. I implemented
two most effective machine learning methods in solving two aspects of this study
namely for social-media-based forecasting by Random forests model and e-commerce
forecasting by an Advanced ARIMA and LSTM model. | en_US |
