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Papers/Fine-grained Sentiment Classification using BERT

Fine-grained Sentiment Classification using BERT

Manish Munikar, Sushil Shakya, Aakash Shrestha

2019-10-04Chinese Sentiment AnalysisSentiment AnalysisTransfer LearningSentiment ClassificationGeneral ClassificationClassification
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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.

Results

TaskDatasetMetricValueModel
Sentiment AnalysisSST-5 Fine-grained classificationAccuracy55.5BERT Large
Sentiment AnalysisSST-5 Fine-grained classificationAccuracy53.2BERT Base
Sentiment AnalysisSST-2 Binary classificationAccuracy91.2BERT Base

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