Sentiment Analysis of E-commerce Consumer Based on Product Delivery Time Using Machine Learning

Jahan, Hasnur; Bitto, Abu Kowshir; Arman, Md. Shohel; Mahmud, Imran; Hossain, Shah Fahad; Saha, Rakhi Moni; Shohug, Md. Mahfuj Hasan

doi:10.1007/978-981-19-2347-0_51

Cited by 7 publications

(1 citation statement)

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“…Many papers, publications, and research projects concentrate on text classification, recognition, and categories, while others focus on specific language and context [12]- [14]. Transfer learning also applied to recognition and categories Bangla words [15].…”

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confidence: 99%

Sentiment analysis from Bangladeshi food delivery startup based on user reviews using machine learning and deep learning

et al. 2023

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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.

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confidence: 99%