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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| Published in: | 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) pp. 1 - 6 |
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| Main Authors: | , |
| Format: | Conference Proceeding |
| Language: | English |
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IEEE
13.09.2022
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Rabiul Alam, Md. Golam Shakil, Mahmudul Hasan |
| Author_xml | – sequence: 1 givenname: Mahmudul Hasan surname: Shakil fullname: Shakil, Mahmudul Hasan email: mahmudul.hasan.shakil@g.bracu.ac.bd organization: BRAC University,Department of Computer Science and Engineering,Dhaka,Bangladesh – sequence: 2 givenname: Md. Golam surname: Rabiul Alam fullname: Rabiul Alam, Md. Golam email: rabiul.alam@bracu.ac.bd organization: BRAC University,Department of Computer Science and Engineering,Dhaka,Bangladesh |
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| Snippet | Data innovation has advanced rapidly in recent years, and the network media has undergone several problematic changes. Places where consumers can express their... |
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| SubjectTerms | 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 |
| Title | Toxic Voice Classification Implementing CNN-LSTM & Employing Supervised Machine Learning Algorithms Through Explainable AI-SHAP |
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