• Renu Kumari
• Vijay Kumar

Sentiment analysis, or opinion mining, is a critical Natural Language Processing (NLP) technique that discerns the emotional tone in textual content, categorizing it as positive, negative, or neutral. It finds extensive application in understanding public sentiments on social media platforms like Twitter, Facebook, and Instagram. However, the proliferation of hate speech on these platforms poses a pressing issue, promoting violence, discrimination, and prejudice. This paper addresses the challenge of hate speech on Twitter, a widely utilized micro-blogging platform. Many methods have already been created to automate hate speech detection online. This process has two elements: identifying the qualities these terms utilize to target a specific group and classifying textual material as hate or non-hate speech. Detecting hate speech is more challenging, as our research of the language used in typical datasets reveals that hate speech lacks distinctive, discriminatory characteristics. In this paper, we present a novel approach that involves classifying tweets into three categories: "sexism," "racism," or "none." By doing so, we aim to detect and categorize instances of harmful content on Twitter. Our work contributes to sentiment analysis and offers a practical solution to identify and combat hate speech on a platform with significant societal influence. Machine learning methods are beneficial for capturing the meaning of hate speech and are thus proposed as feature extractors. Data from social media sites such as Twitter are used to test the effectiveness of these procedures, and they reveal a significant improvement in macro-average F1 and 9% improvement for content labeled as hateful, respectively.

Hate speech · Machine learning · Online social networks · NLP · Text classification · Social media.

"Automated Detection of Hate Speech and Sentiment Analysis on Twitter using Machine Learning Techniques⋆", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.b47-b58, February-2024, Available :http://www.jetir.org/papers/JETIR2402105.pdf

"Automated Detection of Hate Speech and Sentiment Analysis on Twitter using Machine Learning Techniques⋆", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppb47-b58, February-2024, Available at : http://www.jetir.org/papers/JETIR2402105.pdf