Logistic Regression with TF-IDF achieved 73.5% accuracy and outperformed BiLSTM at 69.17% on a 10k-tweet subset of Sentiment140, with the deep model showing mild overfitting.
Bidirectional recurrent neural networks.IEEE transactions on Signal Processing, 45(11):2673–2681
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BiLSTM achieves 89% accuracy and 0.89 weighted F1 on 20-class emotion detection, marginally outperforming SVM at 88.11% on a 79,595-sentence dataset.
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A Comparative Analysis of Machine Learning and Deep Learning Models for Tweet Sentiment Classification: A Case Study on the Sentiment140 Dataset
Logistic Regression with TF-IDF achieved 73.5% accuracy and outperformed BiLSTM at 69.17% on a 10k-tweet subset of Sentiment140, with the deep model showing mild overfitting.
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Benchmarking PyCaret AutoML Against BiLSTM for Fine-Grained Emotion Classification: A Comparative Study on 20-Class Emotion Detection
BiLSTM achieves 89% accuracy and 0.89 weighted F1 on 20-class emotion detection, marginally outperforming SVM at 88.11% on a 79,595-sentence dataset.