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Fine-grained Sentiment Classification using BERT

2019-10-04Code Available0· sign in to hype

Manish Munikar, Sushil Shakya, Aakash Shrestha

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Abstract

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.

Tasks

Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
SST-2 Binary classificationBERT BaseAccuracy91.2Unverified
SST-5 Fine-grained classificationBERT LargeAccuracy55.5Unverified
SST-5 Fine-grained classificationBERT BaseAccuracy53.2Unverified

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