Online media are well-known to be suitable for conveying hate speech. Hateful wording as such involves communications that unlawfully demean any group or person based on certain characteristics, including colour, race, gender, ethnicity, sexual orientation, religion, or nationality. The continuing rise of internet social platforms, including micro blogging services like Twitter, has compelled the need for more immediate analyses of hatreds and other antagonistic responses to various trigger events. This study aims to investigate the details using aspect-based inspections of sentiments. Content analysis of such tweets along with the associations between them is key. Nevertheless, due to the large data volumes involved, it can oftentimes be burdensome if not infeasible to conduct these types of analyses manually. The main problems of prior methods involve data sparsity, classification accuracy, and sarcastic content identification. for the techniques incorrectly categorise tweets as neutral. For content analysis, three dissimilar schemes were suggested, with all proposing to surmount the above-mentioned problems. The research results show that the proposed strategy has achieved correspondingly increased accuracies of some 75%, 71.43%, and 92.86%.