Women Safety Analysis based on Twitter (Machine Learning)

In today's world, protecting women's safety in public areas is of utmost importance. As social media has grown in popularity, Twitter has emerged as a useful real-time data source reflecting public opinion and incidences pertaining to women's safety. In order to obtain an understanding of the frequency and character of conversations pertaining to women's safety, this study uses machine learning techniques to examine data from Twitter. Using pertinent hashtags and keywords, a sizable dataset of tweets on women's safety is gathered as part of the suggested technique. The text data will be preprocessed using Natural Language Processing (NLP) techniques in order to extract significant characteristics and classify tweets as positive, negative, or neutral using sentiment analysis. Additionally, the usage of Named Entity Recognition will be investigated to forecast the sentiment and context of tweets, machine learning models—such as classification algorithms—will be trained on labeled datasets. The purpose of this investigation is to pinpoint hotspots, patterns, and trends pertaining to women's safety issues. Furthermore, temporal analysis will be performed to track changes in event frequency over time. By using Twitter data, the research will help construct a predictive algorithm that can detect any safety hazards. Law enforcement agencies, legislators, and community organizations may find the study's findings useful in putting preventive measures in place to improve women's safety.