Comparison of Deep Learning Models and Various Text Pre-Processing Techniques for the Toxic Comments Classification

Maslej-Krešňáková, Viera; Sarnovský, Martin; Butka, Peter; Machová, Kristína

doi:10.3390/app10238631

Cited by 66 publications

(27 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The obstacle of the two studies lies in the small amount of data so that additional data is needed for further research development. On the other hand, the use of pre-trained models is used to solve other NLP problems such as text based emoticon classification [2] and toxic comment classification [30] using RoBERTa and XLNet shows improvement in accuracy when added with other NLP Features such as TF-IDF and sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

et al. 2021

View full text Add to dashboard Cite

The ever-increasing social media users has dramatically contributed to significant growth as far as the volume of online information is concerned. Often, the contents that these users put in social media can give valuable insights on their personalities (e.g., in terms of predicting job satisfaction, specific preferences, as well as the success of professional and romantic relationship) and getting it without the hassle of taking formal personality test. Termed personality prediction, the process involves extracting the digital content into features and mapping it according to a personality model. Owing to its simplicity and proven capability, a well-known personality model, called the big five personality traits, has often been adopted in the literature as the de facto standard for personality assessment. To date, there are many algorithms that can be used to extract embedded contextualized word from textual data for personality prediction system; some of them are based on ensembled model and deep learning. Although useful, existing algorithms such as RNN and LSTM suffers from the following limitations. Firstly, these algorithms take a long time to train the model owing to its sequential inputs. Secondly, these algorithms also lack the ability to capture the true (semantic) meaning of words; therefore, the context is slightly lost. To address these aforementioned limitations, this paper introduces a new prediction using multi model deep learning architecture combined with multiple pre-trained language model such as BERT, RoBERTa, and XLNet as features extraction method on social media data sources. Finally, the system takes the decision based on model averaging to make prediction. Unlike earlier work which adopts a single social media data with open and close vocabulary extraction method, the proposed work uses multiple social media data sources namely Facebook and Twitter and produce a predictive model for each trait using bidirectional context feature combine with extraction method. Our experience with the proposed work has been encouraging as it has outperformed similar existing works in the literature. More precisely, our results achieve a maximum accuracy of 86.2% and 0.912 f1 measure score on the Facebook dataset; 88.5% accuracy and 0.882 f1 measure score on the Twitter dataset.

show abstract

Section: Related Workmentioning

confidence: 99%

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Then, the performance calculations can be achieved with two operations, namely micro-averaging and macro-averaging. Because of the imbalance in the class dataset, we used micro-averaging in this study [36]. To plot the ROC curve, the required TPR and FPR are computed as follows: where i stands for each testing class and n will be 15 classes for this study.…”

Section: Discussionmentioning

confidence: 99%

The Evaluation of Acute Myeloid Leukaemia (AML) Blood Cell Detection Models Using Different YOLO Approaches

Naing

Kittichai

Tongloy

et al. 2021

Preprint

View full text Add to dashboard Cite

This study proposes to evaluate the performance of Acute Myeloid Leukaemia (AML) blast cell detection models in microscopic examination images for faster diagnosis and disease monitoring. One of the popular deep learning algorithms such as You Only Look Once (YOLO) developed for object detection is the successful state-of-the-art algorithms in real-time object detection systems. We employ four versions of the YOLO algorithm: YOLOv3, YOLOv3-Tiny, YOLOv2 and YOLOv2-Tiny for detection of 15-class of AML blood cells in examination images. We also acquired the publicly available dataset from The Cancer Imaging Archive (TCIA), which consists of 18,365 expert-labelled single-cell images. Data augmentation techniques are additionally applied to enhance and balance the training images in the dataset. The overall results indicated that four types of YOLO approach have outstanding performances of more than 92% in precision and sensitivity. In comparison, YOLOv3 has more reliable performance than the other three approaches. Consistently, the AUC values for the four YOLO models are 0.969 (YOLOv3), 0.967 (YOLOv3-Tiny), 0.963 (YOLOv2), and 0.948 (YOLOv2-Tiny). Furthermore, we compare the best model's performance between approaches that use the entire training dataset without using data augmentation techniques and image division with data augmentation techniques. Remarkably, by using 33.51 percent of the training data in model training, the prediction outcomes from the model that used image partitioning with data augmentation were similar to those obtained using the complete training dataset. This work potentially provides a beneficial digital rapid tool in the screening and evaluation of numerous haematological disorders.

show abstract

“…Liu et al [23] proposed a model for multi-label text classification using ELMo and attention with GRU on Kaggle's toxic comment classification data. Krešnáková et al [24] carried out experimentation on the same Kaggle's dataset using different text pre-processing technique with DL models such as CNN, GRU, Bi-LSTM + CNN, Bi-GRU + CNN. Özel et al [26] implemented various ML models such as SVM, kNN etc.…”

Section: Related Studiesmentioning

confidence: 99%

A Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social media

Kumar

Sachdeva

2021

World Wide Web

View full text Add to dashboard Cite

As a constructive mode of information sharing, collaboration and communication, social media platforms offer users with limitless opportunities. The same hypermedia can be transposed into a synthetic and toxic milieu that provides an anonymous, destructive pedestal for online bullying and harassment. Automatic cyberbullying detection on social media using synthetic or real-world datasets is one of a proverbial natural language processing problem. Analyzing a given text requires capturing the existent semantics, syntactic and spatial relationships. Learning representative features automatically using deep learning models efficiently captures the contextual semantics and word order arrangement to build robust and superlative predictive models. This work puts forward a hybrid model, Bi-GRU-Attention-CapsNet (Bi-GAC), that benefits by learning sequential semantic representations and spatial location information using a Bi-GRU with self-attention followed by CapsNet for cyberbullying detection in the textual content of social media. The proposed Bi-GAC model is evaluated for performance using F1-score and ROC-AUC curve as metrics. The results show a superior performance to the existing techniques on the benchmark Formspring.me and MySpace datasets. In comparison to the conventional models, an improvement of nearly 9% and 3% in F-score is observed for MySpace and Formspring.me dataset respectively.

show abstract

Comparison of Deep Learning Models and Various Text Pre-Processing Techniques for the Toxic Comments Classification

Cited by 66 publications

References 42 publications

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

The Evaluation of Acute Myeloid Leukaemia (AML) Blood Cell Detection Models Using Different YOLO Approaches

A Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social media

Contact Info

Product

Resources

About