Quantitative Sentiment Analysis of Women's Safety Using Twitter Data: An NLP Approach

Abstract

In today’s world of digitalization, social media platforms like Twitter serve as important outlets where people express their concerns and opinions on various societal issues including safety. Women's safety remains a significant concern, particularly in urban areas where incidents are often discussed publicly on social platforms. Despite the growing availability of such data there is a gap between analyzing social media sentiment and applying it to understand real-world safety issues. The issue lies in the underutilization of social media analytics for meaningful insights into public perceptions and potential policy interventions regarding women's safety. Twitter had 335.7 million monthly active users (MAU) in 2024, down 5.14% from 2023. There were 368.4 million users in 2022, the largest amount to date. Between 2018 and 2021, approximately 2% of all tweets originating from India contained elements of misogynistic language or sentiment. This research addresses that gap by conducting a comprehensive sentiment analysis of tweets related to women's safety identifying public sentiment patterns and highlighting the critical areas that require attention. Using NLP to process the data and to classify sentiments, this research paper will provide actionable insights. The paper is organized into several sections including a literature review, methodology, results and implications of Women Safety. Each section is designed to provide a comprehensive understanding of the research process from data collection to the implementation of the code and the interpretation of results. The findings from this analysis can support the development of real-time safety monitoring systems and addressing women’s safety concerns more effectively.