Papers › Fine-grained Sentiment Classification using BERT
Fine-grained Sentiment Classification using BERT
Manish Munikar, Sushil Shakya, Aakash Shrestha
Sentiment classification is an important process in understanding people's perception towards a product, service, or topic. Many natural language processing models have been proposed to solve the sentiment classification problem. However, most of them have focused on binary sentiment classification. In this paper, we use a promising deep learning model called BERT to solve the fine-grained sentiment classification task. Experiments show that our model outperforms other popular models for this task without sophisticated architecture. We also demonstrate the effectiveness of transfer learning in natural language processing in the process.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | SST-2 Binary classification | BERT Base | Accuracy | 91.2 | #58 of 87 | Archive leaderboard | report |
| Sentiment Analysis | SST-5 Fine-grained classification | BERT Large | Accuracy | 55.5 | #6 of 31 | Archive leaderboard | report |
| Sentiment Analysis | SST-5 Fine-grained classification | BERT Base | Accuracy | 53.2 | #13 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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