Imbalanced Toxic Comments Classification Using Data Augmentation and Deep Learning

Ibrahim, Marc; Torki, Marwan; El-Makky, Nagwa M.

doi:10.1109/icmla.2018.00141

Cited by 76 publications

(28 citation statements)

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“…The authors use a deep learning-based toxic comments classification approach in [11] for the imbalanced toxic dataset. The performance evaluation is carried out on Kaggle Wikipedia's talk page edits dataset which contains 159,571 records of toxic comments.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification Using RVVC Model

et al. 2021

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Social media platforms and microblogging websites have gained accelerated popularity during the past few years. These platforms are used for expressing views and opinions about products, personalities, and events. Often during discussions and debates, fights take place on social media platforms which involves using rude, disrespectful, and hateful comments called toxic comments. The identification of toxic comments has been regarded as an essential element for social media platforms. This study introduces an ensemble approach, called regression vector voting classifier (RVVC), to identify the toxic comments on social media platforms. The ensemble merges the logistic regression and support vector classifier under soft voting criteria. Several experiments are performed on the imbalanced and balanced dataset to analyze the performance of the proposed approach. For data balance, the synthetic minority oversampling technique (SMOTE) is used on the imbalanced dataset. Furthermore, two feature extraction approaches are utilized to investigate their suitability such as term frequency-inverse document frequency (TF-IDF) and bag-of-words (BoW). The performance of the proposed approach is compared with several machine learning classifiers using accuracy, precision, recall, and F1-score. Results suggest that RVVC outperforms all other individual models when TF-IDF features are used with SMOTE balanced dataset and achieves an accuracy of 0.97.

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Section: Literature Reviewmentioning

confidence: 99%

Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification Using RVVC Model

et al. 2021

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“…Ibrahim et al [49] proposed a toxic detection model based on convolutional neural network (CNN), bidirectional gated recurrent units (GRU), and bidirectional long shortterm memory (LSTM). They used a Wikipedia dataset to evaluate the proposed method and achieved an F-1-score of 87.2% for predicting toxicity types and 82.2% for the classification of toxic/non-toxic.…”

Section: Related Work 21 Applications Of User Generated Contents In Social Mediamentioning

confidence: 99%

Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

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et al. 2021

Electronics

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Social media has become an essential facet of modern society, wherein people share their opinions on a wide variety of topics. Social media is quickly becoming indispensable for a majority of people, and many cases of social media addiction have been documented. Social media platforms such as Twitter have demonstrated over the years the value they provide, such as connecting people from all over the world with different backgrounds. However, they have also shown harmful side effects that can have serious consequences. One such harmful side effect of social media is the immense toxicity that can be found in various discussions. The word toxic has become synonymous with online hate speech, internet trolling, and sometimes outrage culture. In this study, we build an efficient model to detect and classify toxicity in social media from user-generated content using the Bidirectional Encoder Representations from Transformers (BERT). The BERT pre-trained model and three of its variants has been fine-tuned on a well-known labeled toxic comment dataset, Kaggle public dataset (Toxic Comment Classification Challenge). Moreover, we test the proposed models with two datasets collected from Twitter from two different periods to detect toxicity in user-generated content (tweets) using hashtages belonging to the UK Brexit. The results showed that the proposed model can efficiently classify and analyze toxic tweets.

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“…Therefore, the F1-score is a special measure that can evaluate the performance of a model through the trade-off between precision and recall. Additionally, the F1-score gives a better view of ML model performance, especially for datasets with an imbalanced class distribution, because F1-score is not biased towards majority classes [50]. This study evaluated the classification performance of ML models using the F1-score based on the confusion matrix.…”

Section: The Performance Evaluation Metrics Of ML Modelsmentioning

confidence: 99%

Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method

et al. 2021

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Changes in hydrological characteristics and increases in various pollutant loadings due to rapid climate change and urbanization have a significant impact on the deterioration of aquatic ecosystem health (AEH). Therefore, it is important to effectively evaluate the AEH in advance and establish appropriate strategic plans. Recently, machine learning (ML) models have been widely used to solve hydrological and environmental problems in various fields. However, in general, collecting sufficient data for ML training is time-consuming and labor-intensive. Especially in classification problems, data imbalance can lead to erroneous prediction results of ML models. In this study, we proposed a method to solve the data imbalance problem through data augmentation based on Wasserstein Generative Adversarial Network (WGAN) and to efficiently predict the grades (from A to E grades) of AEH indices (i.e., Benthic Macroinvertebrate Index (BMI), Trophic Diatom Index (TDI), Fish Assessment Index (FAI)) through the ML models. Raw datasets for the AEH indices composed of various physicochemical factors (i.e., WT, DO, BOD5, SS, TN, TP, and Flow) and AEH grades were built and augmented through the WGAN. The performance of each ML model was evaluated through a 10-fold cross-validation (CV), and the performances of the ML models trained on the raw and WGAN-based training sets were compared and analyzed through AEH grade prediction on the test sets. The results showed that the ML models trained on the WGAN-based training set had an average F1-score for grades of each AEH index of 0.9 or greater for the test set, which was superior to the models trained on the raw training set (fewer data compared to other datasets) only. Through the above results, it was confirmed that by using the dataset augmented through WGAN, the ML model can yield better AEH grade predictive performance compared to the model trained on limited datasets; this approach reduces the effort needed for actual data collection from rivers which requires enormous time and cost. In the future, the results of this study can be used as basic data to construct big data of aquatic ecosystems, needed to efficiently evaluate and predict AEH in rivers based on the ML models.

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Imbalanced Toxic Comments Classification Using Data Augmentation and Deep Learning

Cited by 76 publications

References 1 publication

Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification Using RVVC Model

Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification Using RVVC Model

Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method

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