## 3 Results
### Experimental Results of the Machine Learning Based Approach
This is achieved by incorporating a threshold parameter to align the training dataset with the annotation process. Listing 1 details a novel approach to text auto-annotation. This approach employs semi-supervised and self-learning strategies. The initial steps involve dataset input. It includes an annotated dataset featuring scenarios of varied training proportions and a larger unannotated dataset. Preprocessing involves transforming both datasets into a semi-structured format. This process paves the way for subsequent feature extraction through TF-IDF and Word2 Vec vectorization methods. The subsequent steps involve creating classifier models using SVM, DT, KNN, and NB. The best model is then validated through a semi-supervised approach using the Semi-Structured Annotated Dataset (SSADF). The pseudocode incorporates a thoughtful strategy of creating a model using a subset of the annotated dataset for validation, thus ensuring robust model performance. This process focuses on a semi-supervised validation. It involves converting datasets and iteratively refining the annotation results until a satisfactory threshold is achieved. The final output of SSADF reflects the culmination of this intricate process, showcasing the model's ability to autonomously annotate text data. Figure 5: Accuracy of the proposed automatic annotation process for detecting hate speech using a semi-supervised and self-learning approach based on scenarios 9, 10, 11, and 12 (Table 1). ``` STEPS :
1. Dataset Input: 1. Annotated Dataset (20%) with scenarios training 5%:10%:20% 2. Unannotated dataset (80%)
2. Preprocessing 1. Annotated Dataset \(\rightarrow\) semi structured Annotated Dataset 2. Unannotated dataset \(\rightarrow\) semi structured Unannotated Dataset
3. Vectorization using merged semi structured annotated dataset + unannotated dataset : TF-IDF, Word2 Vec
4. Put 10% from semi structured annotated dataset for sample dataset
5. Create a model classifier with 80% data training and 20% data validation. Model using SVM, DT, KNN, NB
6. Get the best model MTA (Multi-class Text Annotation): vectorizer + machine learning (with the best parameter) ================================================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================ ================  ================ ================ ================



Comparative results, detailed in Table 11, underscore the effectiveness of our proposed scenarios. The performance metrics reveal substantial enhancements compared to those reported in Refs. [5; 56]. Table 12 quantifies these improvements, providing an apparent percentage increase over the baseline results presented in Refs. [5; 56]. #### 3.3.2 Accuracy Trends and Method Performance

This section examines the accuracy trends and performance of various methods employed in hate speech detection scenarios. Our study adopts a semi-supervised learning approach, as depicted in Figure 1. It involves annotating unlabeled data to refine the sample dataset and automate the hate speech annotation process. The systematic progression across varying scenarios and threshold values demonstrates a discernible impact on the accuracy of the methods. This impact is evident in the outcomes of our study.