Sentiment Analysis: Predicting Product Reviews for E-Commerce Recommendations Using Deep Learning and Transformers

Oumaima Bellar (), Amine Baina and Mostafa Ballafkih
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Oumaima Bellar: RAISS Team, STRS Lab, National Institute of Posts and Telecommunications, Rabat 10000, Morocco
Amine Baina: RAISS Team, STRS Lab, National Institute of Posts and Telecommunications, Rabat 10000, Morocco
Mostafa Ballafkih: RAISS Team, STRS Lab, National Institute of Posts and Telecommunications, Rabat 10000, Morocco

Mathematics, 2024, vol. 12, issue 15, 1-21

Abstract: The abundance of publicly available data on the internet within the e-marketing domain is consistently expanding. A significant portion of this data revolve around consumers’ perceptions and opinions regarding the goods or services of organizations, making it valuable for market intelligence collectors in marketing, customer relationship management, and customer retention. Sentiment analysis serves as a tool for examining customer sentiment, marketing initiatives, and product appraisals. This valuable information can inform decisions related to future product and service development, marketing campaigns, and customer service enhancements. In social media, predicting ratings is commonly employed to anticipate product ratings based on user reviews. Our study provides an extensive benchmark comparison of different deep learning models, including convolutional neural networks (CNN), recurrent neural networks (RNN), and bi-directional long short-term memory (Bi-LSTM). These models are evaluated using various word embedding techniques, such as bi-directional encoder representations from transformers (BERT) and its derivatives, FastText, and Word2Vec. The evaluation encompasses two setups: 5-class versus 3-class. This paper focuses on sentiment analysis using neural network-based models for consumer sentiment prediction by evaluating and contrasting their performance indicators on a dataset of reviews of different products from customers of an online women’s clothes retailer.

Keywords: artificial intelligence; sentiment analysis/rating; natural language processing; deep learning; transformers; product reviews (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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