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
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