Toxic Comment Classification based on Personality Traits Using NLP

Concerns about the frequency of harmful remarks have been raised by the growth of online communication platforms, which makes it difficult to create inclusive and safe digital spaces. This study explores the creation of a strong framework that uses machine learning algorithms and natural language processing (NLP) methods to categorise harmful comments. In order to improve the accuracy and comprehensiveness of categorization, the study investigates the integration of personality trait analysis in addition to identifying hazardous language. A wide range of online comments comprised the dataset that was gathered and put through extensive preparation methods such as text cleaning, lemmatization, and feature extraction. To facilitate the training and assessment of machine learning models, textual data was converted into numerical representations by utilising TF-IDF vectorization and word embeddings. Furthermore, personality traits were extracted from comments using sentiment analysis and language clues, which linked linguistic patterns with behavioural inclinations. The study resulted in the development and assessment of complex categorization models that combined features from textual content and inferred personality traits. The findings show encouraging associations between specific personality qualities and the use of toxic language, providing opportunities to identify subtle differences in toxic comment contexts. In order to provide insights into developing more sophisticated and successful methods of reducing toxicity in online discourse, this study outlines the methodology, major findings, and consequences of incorporating personality traits analysis into the classification of toxic comments

LLMs and Finetuning: Benchmarking cross-domain performance for hate speech detection

This paper compares different pre-trained and fine-tuned large language models (LLMs) for hate speech detection. Our research underscores challenges in LLMs' cross-domain validity and overfitting risks. Through evaluations, we highlight the need for fine-tuned models that grasp the nuances of hate speech through greater label heterogeneity. We conclude with a vision for the future of hate speech detection, emphasizing cross-domain generalizability and appropriate benchmarking practices.Comment: 9 pages, 3 figures, 4 table

Thai culture image classification with transfer learning

Classifying images of Thai culture is important for a variety of applications, such as tourism, education, and cultural preservation. However, building a Machine learning model from scratch to classify Thai cultural images can be challenging due to the limited availability of annotated data. In this study, we investigate the use of transfer learning for the task of image classification on a dataset of Thai cultural images. We utilize three popular convolutional neural network models, namely MobileNet, EfficientNet, and residual network (ResNet) as baseline pre-trained models. Their performances were evaluated when they were trained from random initialization, used as a feature extractor, and fully fine-tuned. The results showed that all three models performed better in terms of accuracy and training time when they were used as a feature extractor, with EfficientNet achieving the highest accuracy of 95.87% while maintaining the training time of 24 ms/iteration. To better understand the reasoning behind the predictions made by the models, we deployed the gradient-weighted class activation mapping (Grad-CAM) visualization technique to generate heatmaps that the models attend to when making predictions. Both our quantitative and qualitative experiments demonstrated that transfer learning is an effective approach to image classification on Thai cultural images

IMPLEMENTASI BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS (BERT) UNTUK MENDETEKSI HATESPEECH DAN ABUSIVE LANGUAGE PADA TWITTER BAHASA INDONESIA

Ujaran kebencian dan bahasa kasar di media sosial merupakan salah satu penyebab terjadinya konflik di masyarakat. Terdapatnya ujaran kebencian dan bahasa kasar pada media sosial dikarenakan pada media sosial pengguna dapat dengan bebas untuk menyampaikan pendapatnya, oleh karena itu konten ujaran kebencian dan bahasa kasar pada media sosial perlu dideteksi dan dibatasi. Pada penelitian [1] telah dilakukan penelitian dalam mendeteksi hatespeech dan abusive beserta target, kategori dan level hatespeech menggunakan berbagai feature extraction, classifier dan transformasi data. Pada penelitian tersebut word unigram, Random Forest Decision Tree dan label power-set merupakan kombinasi terbaik dengan akurasi 66.12%. Pada penelitian tersebut belum didapatkan hasil yang optimal dalam mendeteksi hatespeech dan abusive beserta target, kategori dan level hatespeech. Beberapa tahun belakangan ini neural network yang dikombinasikan dengan pretained language model seperti Bidirectional encoder from transformers (BERT) mendapatkan akurasi yang baik dalam berbagai tugas natural language processing. Pada penelitian ini dilakukan penelitian dengan membuat model neural network dengan BERT untuk mengklasifikasi hatespeech dan abusive language beserta target, kategori dan level. Hasil dari penelitian ini didapatkan bahwa model neural network dengan BERT mendapatkan hasil yang lebih baik dari penelitian sebelumnya yaitu dengan akurasi 72.28

Deep learning for religious and continent-based toxic content detection and classification

With time, numerous online communication platforms have emerged that allow people to express themselves, increasing the dissemination of toxic languages, such as racism, sexual harassment, and other negative behaviors that are not accepted in polite society. As a result, toxic language identification in online communication has emerged as a critical application of natural language processing. Numerous academic and industrial researchers have recently researched toxic language identification using machine learning algorithms. However, Nontoxic comments, including particular identification descriptors, such as Muslim, Jewish, White, and Black, were assigned unrealistically high toxicity ratings in several machine learning models. This research analyzes and compares modern deep learning algorithms for multilabel toxic comments classification. We explore two scenarios: the first is a multilabel classification of Religious toxic comments, and the second is a multilabel classification of race or toxic ethnicity comments with various word embeddings (GloVe, Word2vec, and FastText) without word embeddings using an ordinary embedding layer. Experiments show that the CNN model produced the best results for classifying multilabel toxic comments in both scenarios. We compared the outcomes of these modern deep learning model performances in terms of multilabel evaluation metrics

Hate speech and offensive language detection: a new feature set with filter-embedded combining feature selection

Social media has changed the world and play an important role in people lives. Social media platforms like Twitter, Facebook and YouTube create a new dimension of communication by providing channels to express and exchange ideas freely. Although the evolution brings numerous benefits, the dynamic environment and the allowable of anonymous posts could expose the uglier side of humanity. Irresponsible people would abuse the freedom of speech by aggressively express opinion or idea that incites hatred. This study performs hate speech and offensive language detection. The problem of this task is there is no clear boundary between hate speech and offensive language. In this study, a selected new features set is proposed for detecting hate speech and offensive language. Using Twitter dataset, the experiments are performed by considering the combination of word n-gram and enhanced syntactic n-gram. To reduce the feature set, filter-embedded combining feature selection is used. The experimental results indicate that the combination of word n-gram and enhanced syntactic n-gram with feature selection to classify the data into three classes: hate speech, offensive language or neither could give good performance. The result reaches 91% for accuracy and the averages of precision, recall and F1