ANALYSIS OF WOMEN SAFETY IN INDIAN CITIES USING MACHINE LEARNING ON TWEETS

Abstract

Women’s safety remains a critical social concern in India, with incidents of harassment, violence, and insecurity affecting mobility, well-being, and quality of life. The widespread use of social media platforms has generated vast amounts of user-generated content that reflects public perceptions, experiences, and concerns regarding women’s safety across different cities. This paper presents an analysis of women’s safety in Indian cities using machine learning techniques on Twitter data. The proposed framework collects and analyzes tweets related to women’s safety, crime incidents, public sentiment, and urban security issues. Natural Language Processing (NLP) and machine learning algorithms are employed to preprocess textual data, extract meaningful features, perform sentiment analysis, and classify safety-related discussions. The analysis identifies patterns, trends, and public opinions associated with women’s safety across various Indian cities. Experimental observations demonstrate that machine learning-based tweet analysis provides valuable insights into societal perceptions, regional safety concerns, and emerging risk factors. The proposed approach supports datadriven decision-making for policymakers, law enforcement agencies, and urban planners in developing effective strategies to enhance women’s safety and security.