Transformer-based approaches to Sentiment Detection

The use of transfer learning methods is largely responsible for the present breakthrough in Natural Learning Processing (NLP) tasks across multiple domains. In order to solve the problem of sentiment detection, we examined the performance of four different types of well-known state-of-the-art transf...

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Bibliographic Details
Published inarXiv.org
Main Authors Olumide Ebenezer Ojo, Hoang Thang Ta, Gelbukh, Alexander, Calvo, Hiram, Adebanji, Olaronke Oluwayemisi, Sidorov, Grigori
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 13.03.2023
Subjects
Algorithms
Coders
Computer architecture
Disasters
Machine learning
Neural networks
Transformers
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Summary:The use of transfer learning methods is largely responsible for the present breakthrough in Natural Learning Processing (NLP) tasks across multiple domains. In order to solve the problem of sentiment detection, we examined the performance of four different types of well-known state-of-the-art transformer models for text classification. Models such as Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pre-training Approach (RoBERTa), a distilled version of BERT (DistilBERT), and a large bidirectional neural network architecture (XLNet) were proposed. The performance of the four models that were used to detect disaster in the text was compared. All the models performed well enough, indicating that transformer-based models are suitable for the detection of disaster in text. The RoBERTa transformer model performs best on the test dataset with a score of 82.6% and is highly recommended for quality predictions. Furthermore, we discovered that the learning algorithms' performance was influenced by the pre-processing techniques, the nature of words in the vocabulary, unbalanced labeling, and the model parameters.
ISSN:2331-8422