Abstract:
Food delivery methods are at the top of the list in today's world. People's
attitudes toward food delivery systems are usually influenced by food quality
and delivery time. We did a sentiment analysis of consumer comments on
the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz
Food, and data was acquired from these four sites’ remarks. In natural
language processing (NLP) task, before the model was implemented, we
went through a rigorous data pre-processing process that included stages like
adding contractions, removing stop words, tokenizing, and more. Four
supervised classification techniques are used: extreme gradient boosting
(XGB), random forest classifier (RFC), decision tree classifier (DTC), and
multi nominal Naive Bayes (MNB). Three deep learning (DL) models are
used: convolutional neural network (CNN), long term short memory
(LSTM), and recurrent neural network (RNN). The XGB model exceeds all
four machine learning (ML) algorithms with an accuracy of 89.64%. LSTM
has the highest accuracy rate of the three DL algorithms, with an accuracy of
91.07%. Among ML and DL models, LSTM DL takes the lead to predict the
sentiment.
