Toxic Voice Classification Implementing CNN-LSTM & Employing Supervised Machine Learning Algorithms Through Explainable AI-SHAP

Data innovation has advanced rapidly in recent years, and the network media has undergone several problematic changes. Places where consumers can express their thoughts through messages, photos, and notes, such as Facebook, Twitter, and Instagram, are gaining popularity. Unfortunately, it has become...

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Vydáno v:2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) s. 1 - 6
Hlavní autoři: Shakil, Mahmudul Hasan, Rabiul Alam, Md. Golam
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 13.09.2022
Témata:
Classification algorithms
CNN-LSTM
Convolutional neural networks
Explainable AI
Extra Trees Algorithm
Logistic Regression
Machine learning algorithms
Media
NLP
Random Forest
SHAP
Stacking
Technological innovation
Tokenization
Word Embedding
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Shrnutí:Data innovation has advanced rapidly in recent years, and the network media has undergone several problematic changes. Places where consumers can express their thoughts through messages, photos, and notes, such as Facebook, Twitter, and Instagram, are gaining popularity. Unfortunately, it has become a place of toxic, insults, cyberbullying, and mysterious dangers. There is a lot of research here, but none has found a sufficient level of accuracy. This paper proposes a Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and Natural Language Processing (NLP) fusion strategy that characterizes malicious and non-malicious remarks with a word embedding technique at an initial stage. And this model can categorize any voice data into six levels of classification. Furthermore, the processed dataset is applied to two traditional Machine Learning Algorithms (Random Forest and Extra Tress Algorithm) with an estimator (Logistic Regression) and interprets these algorithms with an Explainable AI (XAI)-SHAP. In the final step, two classifiers and the estimator are ensembled with Stacking Classifier, which is better than any previous activity.
DOI:10.1109/IICAIET55139.2022.9936775