Summary of Roberta-bilstm: a Context-aware Hybrid Model For Sentiment Analysis, by Md. Mostafizer Rahman et al.
RoBERTa-BiLSTM: A Context-Aware Hybrid Model for Sentiment Analysis
by Md. Mostafizer Rahman, Ariful Islam Shiplu, Yutaka Watanobe, Md. Ashad Alam
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 The proposed RoBERTa-BiLSTM hybrid deep learning model combines the strengths of sequential models and Transformer-based architectures to enhance performance in sentiment analysis tasks. The model leverages RoBERTa’s robust word embedding generation and BiLSTM’s contextual semantics capture capabilities to analyze comments effectively, uncovering latent intentions and making strategic decisions across various domains. Experimental results demonstrate that the RoBERTa-BiLSTM model surpasses baseline models on IMDb, Twitter US Airline, and Sentiment140 datasets, achieving high accuracies and F1-scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to analyze comments to understand people’s intentions. This is important because it can help make good decisions in many areas like business or politics. The problem is that comments are often hard to understand due to the variety of words used, long sentences, and unknown symbols. Most existing methods use sequential models which take longer to process, but this new model uses a parallel processing approach called Transformer. The new model combines two other approaches: RoBERTa, which helps generate good word embeddings, and BiLSTM, which is good at understanding the meaning of words in context. The authors tested their model on three datasets and it performed better than existing models. |
Keywords
» Artificial intelligence » Deep learning » Embedding » Semantics » Transformer
