SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERT
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912006880100352 |
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| author | Das, Rupak Kumar Pedersen, Ted |
| author_facet | Das, Rupak Kumar Pedersen, Ted |
| contents | This paper uses the BERT model, which is a transformer-based architecture, to solve task 4A, English Language, Sentiment Analysis in Twitter of SemEval2017. BERT is a very powerful large language model for classification tasks when the amount of training data is small. For this experiment, we have used the BERT(BASE) model, which has 12 hidden layers. This model provides better accuracy, precision, recall, and f1 score than the Naive Bayes baseline model. It performs better in binary classification subtasks than the multi-class classification subtasks. We also considered all kinds of ethical issues during this experiment, as Twitter data contains personal and sensible information. The dataset and code used in our experiment can be found in this GitHub repository. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_07944 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERT Das, Rupak Kumar Pedersen, Ted Computation and Language This paper uses the BERT model, which is a transformer-based architecture, to solve task 4A, English Language, Sentiment Analysis in Twitter of SemEval2017. BERT is a very powerful large language model for classification tasks when the amount of training data is small. For this experiment, we have used the BERT(BASE) model, which has 12 hidden layers. This model provides better accuracy, precision, recall, and f1 score than the Naive Bayes baseline model. It performs better in binary classification subtasks than the multi-class classification subtasks. We also considered all kinds of ethical issues during this experiment, as Twitter data contains personal and sensible information. The dataset and code used in our experiment can be found in this GitHub repository. |
| title | SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERT |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.07944 |