Violence Detection in Natural Language

Natural language plays an important role in how humans come to hold beliefs. Violence in natural language can be an expression of violent beliefs by the writer and/or lead to violent beliefs in the reader. Beliefs lead inevitably to actions, thus violent texts could lead to violent actions. Violence is a phenomenon that societies are fundamentally concerned with minimizing. Therefore, we are concerned with developing better insights into violence in texts. The large volume of, and pace at which, texts are published in this age calls for automated ways of detection, to gain better insights and to respond quicker. We investigate automated violence detection on texts written in the English language. We are interested in the following two research questions: 1) What is violence? 2) How can we detect violence in an automated fashion? First, we want to develop a better insight into the concept of violence that can assist us with our goal of detecting it automatically. We identify the automated detection of violence in text as a sentiment analysis problem. We investigate automated sentiment analysis on English movie summaries. Our approach consists out of a literature study of the concept of violence and a theoretical analysis of the different approaches of sentiment analysis. For the literature study we will present and discuss multiple philosophical views towards the concept of violence. We argue for an understanding that takes into account both the intentions of the perpetrator, as well as the harm inflicted to the victim. For the sentiment classification problem we discuss lexicon-based, machine learning and deep learning approaches and argue why the last is the most promising approach. We propose a neural network model, using the LSTM architecture, for the sentiment analysis of violence. Furthermore, we introduce a large dataset of movie summaries to enable supervised learning classifiers. We also implement the LIME framework to better interpret the deep learning model. In our experimental results we discuss the properties of our dataset and perform topic modeling to gain better insights into the various topics which are hidden in the dataset. We implement lexicon-based, machine learning and deep learning methods. We give a detailed description of the LSTM model and tune various hyperparameters. We show that our LSTM model performs better than lexicon-based and traditional machine learning classifier baselines. We show that the accuracy of our sentiment analysis is around 75%. Furthermore, we discuss how well our expectations of the literature study match the experimental results and provide examples of intuitive as well as counter-intuitive predictions made by the models. We discuss that, based on our research, violence can not be detected automatically with the same accuracy as we would expect from a human. Furthermore we argue that the accordance of a model with human perception about violence is equally important for the model to be trustworthy. We discuss that a tool for detecting violence can currently serve as supplement to human detection. Next, we discuss possible applications of our approach and how our approach can be improved and/or extended upon in the future.