Due to harmful content, hate speech’s prevalence on social media platforms like twitter poses significant challenges. This paper explores the efficacy of two natural language processing (nlp) methods, semantic similarity and part of speech (pos)-tagging sentiment analysis, in identifying hate speech. Semantic similarity compares the distance between word vectors in a sentence and in a corresponding corpus of a certain type of speech, while sentiment analysis tokenizes particular parts of speech (such as adjectives) in a sentence and identifies how often these emotions appear in the sentence. This study uses a dataset of tweets categorized by crowdflower workers based on whether they contain hate speech, offensive language, or neither, and conducts statistical analyses to compare the effectiveness of these two methods. Results indicate that negative sentiments are most effective in distinguishing disturbing tweets from normal ones, while semantic similarity analysis shows promise in conducting this distinction using a corpus made out of disturbing tweets. However, challenges remain in reliably classifying hate speech, suggesting avenues for future research to refine methods and definitions. Findings from this study contribute to the ongoing discourse on content moderation and online safety, visioning a more accurate system in tagging hate speech/offensive language on online platforms and thereby aiming to foster a more inclusive digital environment.