Automatic Text Preprocessing for Intelligent Dialog Agents

The paper describes a new Text Preprocessing Pipeline based on a Hybrid approach which involve rule-based and stochastic approaches. The presented pipeline is part of a larger project titled Big Data for Multi-Agent Specialized System developed by Network Contacts in collaboration with University of Salerno and other institutional partners. The aim of the project is to build an Hybrid Question Answering System composed by sets of Dialog Bots able to process great volumes of data. Due to the importance of unstructured textual data, a particular focus of the project is on automatic processing of Text. The paper will describe the three main modules of the preprocessing pipeline, which involve a Style Correction Module, a Clitic Decomposition Module and a POS Tagging and Lemmatization Module

Fuzzy-based machine learning for predicting narcissistic traits among Twitter users.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Social media has provided a platform for people to share views and opinions they identify with or which are significant to them. Similarly, social media enables individuals to express themselves authentically and divulge their personal experiences in a variety of ways. This behaviour, in turn, reflects the user’s personality. Social media has in recent times been used to perpetuate various forms of crimes, and a narcissistic personality trait has been linked to violent criminal activities. This negative side effect of social media calls for multiple ways to respond and prevent damage instigated. Eysenck's theory on personality and crime postulated that various forms of crime are caused by a mixture of environmental and neurological causes. This theory suggests certain people are more likely to commit a crime, and personality is the principal factor in criminal behaviour. Twitter is a widely used social media platform for sharing news, opinions, feelings, and emotions by users. Given that narcissists have an inflated self-view and engage in a variety of strategies aimed at bringing attention to themselves, features unique to Twitter are more appealing to narcissists than those on sites such as Facebook. This study adopted design science research methodology to develop a fuzzy-based machine learning predictive model to identify traces of narcissism from Twitter using data obtained from the activities of a user. Performance evaluation of various classifiers was conducted and an optimal classifier with 95% accuracy was obtained. The research found that the size of the dataset and input variables have an influence on classifier accuracy. In addition, the research developed an updated process model and recommended a research model for narcissism classification