Article’s

Crimes against women remain a significant social issue, and social media platforms like Twitter provide real-time insights into public opinion and emotional responses. This research focuses on analyzing sentiments expressed in tweets related to women’s crime news using Natural Language Processing (NLP) techniques. The dataset is collected from Kaggle and includes tweets related to harassment, domestic violence, and gender-based crimes. The data is preprocessed using techniques such as tokenization, stop word removal, and text cleaning. A rule-based sentiment analysis approach is applied to classify tweets into positive, negative, and neutral categories. The results indicate that the majority of tweets express negative sentiment, reflecting public concern, fear, and anger regarding women’s safety. This study highlights the importance of sentiment analysis in understanding public perception and supports policymakers and organizations in improving safety measures.