Detection of Online Sexism Using Lexical Features and Transformer

Social networks are now integral to modern life, offering instant global communication. Unfortunately, this ease of connectivity has also led to the misuse of freedom, with many social media users expressing inappropriate and often sexist comments. In response, the field of natural language processing has actively sought solutions to detect and counteract such content. Our study builds upon our previous work presented during the SemEval 2023 competition. Initial results, achieved using only lexical features, prompted a thorough reevaluation. Recognizing the potential of advanced techniques, we embarked on a comprehensive exploration to enhance our approach. Leveraging our experience, we focused on a feature union strategy. By seamlessly combining the ’twitter-roberta-base-sentiment-latest’ transformer model with established lexical features, we developed a refined methodology that transcends the limitations of individual elements. This approach improved performance and demonstrated the synergistic potential of merging these distinct features. Our improved method revealed significant enhancements, underscoring the effectiveness of our methodology in boosting detection capabilities. It led to a notable improvement of approximately 20% in binary classification accuracy, a substantial leap from our previous results. The strategic blend of linguistic and transformer-based features emerged as the driving force behind this significant advancement.