Summary of A Hybrid Transformer and Attention Based Recurrent Neural Network For Robust and Interpretable Sentiment Analysis Of Tweets, by Md Abrar Jahin et al.
A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets
by Md Abrar Jahin, Md Sakib Hossain Shovon, M. F. Mridha, Md Rashedul Islam, Yutaka Watanobe
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A hybrid framework, TRABSA, is proposed to improve sentiment analysis, addressing challenges with linguistic diversity, generalizability, and explainability. Combining transformer-based architectures, attention mechanisms, and BiLSTM networks, TRABSA leverages RoBERTa-trained on 124M tweets to achieve state-of-the-art accuracy. The framework compares six word-embedding techniques and three lexicon-based labeling techniques, selecting the best for optimal sentiment analysis. With 94% accuracy and significant precision, recall, and F1-score gains, TRABSA outperforms traditional ML and deep learning models, demonstrating consistent superiority and generalizability across diverse datasets. SHAP and LIME analyses enhance interpretability, improving confidence in predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TRABSA is a new way to analyze how people feel about things. Right now, computers are not very good at understanding what people mean when they talk or write online. TRABSA tries to fix this by combining different types of computer programs and training them on millions of tweets from all over the world. This makes it better at guessing how people feel about things. It even works well for languages that are hard for computers to understand. The new way is very good at getting the right answer, which will help us make better decisions when it comes to things like managing resources during a pandemic. |
Keywords
» Artificial intelligence » Attention » Deep learning » Embedding » F1 score » Precision » Recall » Transformer
