(untitled) (International Journal of Food and Nutritional Sciences, 2022)
This study presents a comprehensive analysis of women’s safety in Indian cities through the lens of social media, utilizing machine learning techniques to interpret and analyze tweets related to safety concerns. With the increasing prevalence of gender-based violence and harassment, understanding public sentiment and the specific challenges faced by women is crucial for policy-making and community support initiatives. By employing natural language processing (NLP) algorithms, we systematically extracted and categorized relevant tweets to identify prevalent themes and sentiments regarding women’s safety. Our methodology includes data collection from Twitter, sentiment analysis, and the implementation of machine learning models to classify tweets based on their relevance to safety issues. The findings reveal significant trends and patterns, highlighting areas of concern and public perception of safety in various urban regions. This research not only contributes valuable insights into the discourse surrounding women’s safety in India but also underscores the potential of machine learning and social media analytics as powerful tools for social research and policymaking. Ultimately, this study aims to foster informed discussions and drive initiatives focused on enhancing women’s safety and security in urban environments.