Item Infomation
Title: | Emotion classification of Indonesian Tweets using Bidirectional LSTM |
Authors: | Aaron, Glenn Phillip, LaCasse Bruce, Cox |
Issue Date: | 2023 |
Publisher: | Springer |
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. |
Description: | CC BY |
URI: | https://link.springer.com/article/10.1007/s00521-022-08186-1 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8273 |
Appears in Collections | OER - Công nghệ thông tin |
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