## 5 Discussion  
In our approach, we have integrated diverse features aimed at capturing various aspects of offensive language, such as semantic information, sentiment, and readability. However, we have encountered challenges in capturing intricate contextual nuances, primarily due to the reliance on handcrafted features and potentially oversimplified text representations. To address this, we experimented with DL models utilizing GloVe embeddings, which can effectively capture semantic nuances in the offensive language by leveraging contextual information encoded in the embedding. Despite their strengths, these models may struggle in capturing long-range dependencies and subtle contextual nuances present in the offensive language. Consequently, we further explored transformer-based models, which excelled in capturing contextual nuances, enabling them to understand the subtleties of offensive language usage. By fine-tuning these models on offense detection tasks, we observed improved accuracy in distinguishing offensive language from non-offensive language. However, it is important to note that transformer-based models may necessitate significant computational resources for training and fine-tuning. In our approach to offensive language detection, we carefully selected features tailored to effectively capture the nuances of offensive content. TF-IDF [67] vectors were chosen for their ability to highlight the importance of terms within individual documents relative to a larger corpus. This allows us to identify terms that are distinctive to offensive language, aiming in distinguishing offensive content from non-offensive content based on the frequency and uniqueness of certain terms. Word2 Vec [68] embeddings were incorporated to provide dense vector representations of words based on their contextual usage. By capturing semantic relationships between words, Word2 Vec embeddings enable the model to understand the context in which offensive language is used, thereby enhancing its ability to discern offensive content. Sentiment analysis [69] was integrated to assign sentiment scores to text, allowing the model to identify text with negative sentiment, which is more likely to contain offensive language. This feature serves as an additional indicator of offensive language by considering the underlying emotional tone conveyed by the text. FKRA/FRE [70] scores were included to measure the readability of text, as offensive language may exhibit distinct readability patterns. By capturing these readability differences, the model can use them as additional indicators of offensive language, further improving its detection capabilities. In our second approach of utilizing GloVe embeddings [71], we leveraged their ability to provide distributed representations of words based on their co-occurrence statistics. By capturing semantic relationships between words, GloVe embeddings allow the model to \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Methodology** & **Dataset** & **F1-Score** & **Key Features/Limitations** \\ \hline
[66] & Cat Boost & Davidson Dataset & 0.89 & Handles categorical features, memory intensive \\ \hline
[40] & CNN, LSTM, LSVM & Own Dataset &
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} 88.97\%, 90\% \\ (accuracy) \\ \end{tabular} & High accuracy but not generalizable \\ \hline Best of our proposed approach: & Section 3.3 & Davidson Dataset & 0.89 & Interpretable results, multiple feature types \\ \hline Best of our proposed approach: GloVe with DL models & Section 3.4 & Davidson Dataset & 0.89 & Semantic understanding, different embedding dimensions \\ \hline Best of our proposed approach: & Section 3.5 & Davidson Dataset & 0.90 & Context-aware, state-of-the-art performance \\ Transformer model with neural network & & & & \\ \hline \hline \end{tabular}
\end{table}