Unveiling AI Patterns: An Experimental Evaluation of Machine Learning Based Women's Safety Prediction Strategy

As is common knowledge, there is a long history of harassment of women all throughout the world, beginning with stalking and continuing to more serious types of abuse such as acid attacks, rape, obscenity, pornography, and other forms of abuse. In light of these worries, it has been proposed that the application of machine learning on tweets be utilized in order to do an analysis of the safety of women in urban areas of India. The Sentimental Analysis is employed as the primary idea, and it is carried out by machine learning using input from tweets. This is done to ensure that safety is taken into consideration. Increasing people's awareness can be accomplished by the use of sentiment analysis to tweets. Social media platforms such as Twitter and Instagram, which are well-known for their capacity to distribute information on a worldwide scale, provide women with the opportunity to communicate their feelings to the rest of the world. Because Twitter enables users to effortlessly connect with one another through text, audio, and video, we have listed it as a resource that is of great value. This enables our study to be more persuasive than the opinions of those who are in our immediate vicinity. In this study, a novel model that is referred to as the Artificial Intelligence based Safety Prediction Strategy (AISPS) is presented in order to provide a clear evaluation of the effectiveness of the system that has been recommended. The standard method known as Support Vector Machine (SVM) is utilized in order to do cross-validation on it.