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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Aaron, Glenn | - |
| dc.contributor.author | Phillip, LaCasse | - |
| dc.contributor.author | Bruce, Cox | - |
| dc.date.accessioned | 2023-04-25T03:54:53Z | - |
| dc.date.available | 2023-04-25T03:54:53Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s00521-022-08186-1 | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/8273 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | Emotion classification can be a powerful tool to derive narratives from social media data. Traditional machine learning models that perform emotion classification on Indonesian Twitter data exist but rely on closed-source features. Recurrent neural networks can meet or exceed the performance of state-of-the-art traditional machine learning techniques using exclusively open-source data and models. Specifically, these results show that recurrent neural network variants can produce more than an 8% gain in accuracy in comparison with logistic regression and SVM techniques and a 15% gain over random forest when using FastText embeddings. | vi |
| dc.language.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | Bidirectional LSTM | vi |
| dc.title | Emotion classification of Indonesian Tweets using Bidirectional LSTM | vi |
| dc.type | Book | vi |
| Appears in Collections | OER - Công nghệ thông tin | |
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