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Advisor(s)
Abstract(s)
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.
Description
Keywords
Sentiment analysis Natural language processing Portuguese language Edge computing BERT Transformers . Faculdade de Ciências Exatas e da Engenharia
Citation
Branco, A.; Parada, D.; Silva, M.; Mendonça, F.; Mostafa, S.S.; Morgado-Dias, F. Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing. Electronics 2024, 13, 589. https://doi.org/10.3390/ electronics13030589
Publisher
MDPI