Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing

This study focuses on improving sentiment analysis in restaurant reviews by leveraging transfer learning and transformer-based pre-trained models. This work evaluates the suitability of pre-trained deep learning models for analyzing Natural Language Processing tasks in Portuguese. It also explores t...

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Bibliographic Details
Published in:Electronics (Basel) 2024, Vol.13 (3), p.589
Main Authors: Branco, Alexandre, Parada, Daniel, Silva, Marcos, Mendonça, Fábio, Mostafa, Sheikh Shanawaz, Morgado-Dias, Fernando
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Classifiers
Computational linguistics
Constraints
Customer feedback
Data mining
Datasets
Decision making
Deep learning
Edge computing
Evaluation
Language processing
Machine learning
Natural language interfaces
Natural language processing
Performance evaluation
Platforms
Restaurants
Sentiment analysis
Social media
Transformers
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Summary:This study focuses on improving sentiment analysis in restaurant reviews by leveraging transfer learning and transformer-based pre-trained models. This work evaluates the suitability of pre-trained deep learning models for analyzing Natural Language Processing tasks in Portuguese. It also explores the viability of utilizing edge devices for Natural Language Processing tasks, considering their computational limitations and resource constraints. Specifically, we employ bidirectional encoder representations from transformers and robustly optimized BERT approach, two state-of-the-art models, to build a sentiment review classifier. The classifier’s performance is evaluated using accuracy and area under the receiver operating characteristic curve as the primary metrics. Our results demonstrate that the classifier developed using ensemble techniques outperforms the baseline model (from 0.80 to 0.84) in accurately classifying restaurant review sentiments when three classes are considered (negative, neutral, and positive), reaching an accuracy and area under the receiver operating characteristic curve higher than 0.8 when examining a Zomato restaurant review dataset, provided for this work. This study seeks to create a model for the precise classification of Portuguese reviews into positive, negative, or neutral categories. The flexibility of deploying our model on affordable hardware platforms suggests its potential to enable real-time solutions. The deployment of the model on edge computing platforms improves accessibility in resource-constrained environments.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13030589