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CNN-BiLSTM outperforms AutoML for Indonesian Twitter hate speech detection

By PulseAugur Editorial · [2 sources] ·

This paper compares PyCaret AutoML and a CNN-BiLSTM model for detecting hate speech on Indonesian Twitter. The CNN-BiLSTM model achieved superior performance, with an accuracy of 83.8% and an F1-score of 81.2%, outperforming the best conventional model from PyCaret, Random Forest, which reached 77.2% accuracy and 77.0% F1-score. The study highlights that while PyCaret is effective for conventional benchmarking, the neural network approach is better suited for this specific task due to its ability to capture nuanced linguistic patterns. AI

IMPACT Demonstrates the effectiveness of neural networks over conventional methods for nuanced text classification tasks.

RANK_REASON Academic paper comparing machine learning models for a specific task.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

CNN-BiLSTM outperforms AutoML for Indonesian Twitter hate speech detection

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Tanty Widiyastuti, Mayada, Adisty Syawalda Ariyanto, Luluk Muthoharoh, Ardika Satria, Martin Clinton Tosima Manullang ·

    A Comparative Study of PyCaret AutoML and CNN-BiLSTM for Binary Hate Speech Detection in Indonesian Twitter

    arXiv:2605.04885v1 Announce Type: new Abstract: This paper compares a PyCaret AutoML branch and a CNN-BiLSTM branch for binary hate speech detection on Indonesian Twitter using the HS label from the corpus of Ibrohim and Budi. Both branches share the same preprocessing pipeline s…

  2. arXiv cs.CL TIER_1 English(EN) · Martin Clinton Tosima Manullang ·

    A Comparative Study of PyCaret AutoML and CNN-BiLSTM for Binary Hate Speech Detection in Indonesian Twitter

    This paper compares a PyCaret AutoML branch and a CNN-BiLSTM branch for binary hate speech detection on Indonesian Twitter using the HS label from the corpus of Ibrohim and Budi. Both branches share the same preprocessing pipeline so that the comparison reflects modelling differe…

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