## 1 Introduction  
Hate speech detection is based on the concept of Natural Language Processing (NLP) and sentiment analysis [1, 2]. NLP uses the concept of text mining to perform processing of the specific text [3]. The process requires several stages, one of which is text annotation, so the training process requires data that has been labeled. Text annotation done manually by humans has various disadvantages, such as being expensive, prone to errors, inconsistent labeling, subjectivity, and difficulties in processing large datasets. These factors occur because the person doing the annotation has different shortcomings, such as fatigue, pressure of circumstances and environment, the influence of workload, and the subjectivity. In addition, if many people do the annotation, they must equalize the perception (understanding of words and mindset) when annotating. This process affects the consistency of the annotated labels. These problems can be solved by applying the concept of automatic text annotation using a machine-learning approach. Thus, we developed an automatic annotation model using several machine learning methods such as SVM, DT, KNN, and NB for hate speech detection based on data from social media (YouTube). The data used can impact the learning process and classification of statements as hate speech or not.  
Automatic text annotations can detect hate speech by applying machine learning methods with a semi-supervised learning approach [4, 5]. Hate speech data are annotated using two categories (hate and not hate) [6, 7, 8, 9, 10], and using sentiment analysis methods, in which data are labeled using two or three categories, namely (positive and negative) [11, 12], or (positive, negative, and neutral) [13, 14, 15, 16]. We develop automatic annotations by utilizing a dataset with minimal labeled training data and incorporate self-learning for labels. This approach draws inspiration from AraSenCorpus modeling [17], which employs a semi-supervised framework (long short-term memory with FastText and machine learning) sentiment Analysis in Arabic. In addition, annotations can extend to sentiment and emotion detection through SVM, Random Forest (RF), and NB [17, 18]. In annotating Turkish text, various methods (such as NB, convolutional neural networks (CNN), Bi-LSTM, and SVM) have demonstrated effectiveness [19].  
This research aims to develop the concept of automated text annotations that apply semi-supervised learning and self-learning. The most important original contributions of this research are as follows:  
1. Machine learning modeling uses the meta-vectorizer and meta-classifier methods to determine the best model;
2. Carrying out experiments with self-learning scenarios using thresholds of 0.6, 0.7, and 0.8 in the proposed model and labeled datasets with the proportion of 5%, 10%, and 20%, and with a training data to test data ratio of 80%;
3. Experimental evaluation of the machine learning methods proposed in this study and their comparisons based on the abovementioned scenarios.  
This study conducted experiments to evaluate the machine learning model based on the meta-vectorizer and meta-classifier concepts proposed herein. The proposed models have different accuracy results in each of the proposed scenarios. The minimally labeled data section (5%) yielded the highest accuracy for the proposed model, indicating that this proportion is the most effective.  
The research presented in this paper is organized as follows. In Section 2, we describe the dataset and machine learning methods used in the NLP-based approach for hate speech detection. Section 3 discusses the experiments and results obtained based on the proposed scenarios. Finally, Section 4 provides conclusions of the conducted research.