Comparative Analysis of BERT-base Transformers and Deep Learning Sentiment Prediction Models

Chinnalagu, Anandan; Durairaj, Ashok Kumar

doi:10.1109/smart55829.2022.10047651

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…A comparative analysis of BERT, Hybrid fastText-BiLSTM, and fastText Trigram models to address challenges in achieving more precise sentiment predictions of fake reviews has been performed [25]. Introducing fine-tuned BERT and Hybrid fastText-BiLSTM models for extensive datasets, the study demonstrates that the proposed fine-tuned BERT model outperforms other DL models and gives a 0.91 accuracy result.…”

Section: Literature Reviewmentioning

confidence: 99%

Transformer-Based Language Deep Learning Detection of Fake Reviews on Online Products

Maysara Mazin Badr Alsaad

2024

jes

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In e-commerce, spotting fake reviews is vital for ensuring trust among consumers. However, identifying them poses a challenge because fake reviews are often crafted to seem genuine, and the sheer volume makes thorough checks difficult. Prior methods involve basic strategies like grammar checks or pattern analysis, but they fell short due to fake reviews generation methods are becoming increasingly sophisticated. Even machine learning, while helpful, struggle to pinpoint subtle fake reviews accurately. This has led to a shift toward deep learning algorithms, which show promise in handling the complexities that traditional methods cannot manage accurately. Specifically, transformer models like BERT, RoBERTa, and XLNet have emerged as potential solutions. This study evaluates the effectiveness of these models in distinguishing between human-generated and computer-generated reviews. RoBERTa displays high accuracy but requires longer learning periods, while BERT and XLNet offer decent accuracy with varying error rates. The investigation delves into these deep learning models to ascertain their capability to spot fake reviews across different scenarios within online platforms. RoBERTa achieves the highest accuracy among the models, reaching 97.1%. It also demonstrates a lower Type I error rate at 2.2%, although its Type II error remains at a moderate level.

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Section: Literature Reviewmentioning

confidence: 99%