Analyzing the Impact of Domestic Violence on Social Media using Natural Language Processing

Due to the rapid advancement in social media and technology, it generates a large amount of data in different areas of applications. Social media analysis and text mining are all about collecting the most valuable data and drawing actionable conclusions. Text mining also referred to as data mining it is which contains various nodes in the form of data which is often linked together to form a pattern. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. In this study we have analyzed and mounted social media data from Twitter, new articles, and Reddit which suggest that domestic abuse is acting as an opportunistic infection, flourishing in the condition created by the pandemic. The computing tweet sentiments of domestic violence amongst various social media platforms is a major factor of concern. We have used several topic modeling techniques such as Latent Semantic Analysis (LSA) uses a bag of words model, Hierarchical Dirichlet Process (HDP) is a nonparametric Bayesian model for clustering problems, and Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data. Therefore, in this project, we tend to propose a deeper insight into the rise in domestic violence on social media and to provide a holistic approach to tackle this situation.