Automated Crime Tweets Classification and Geo-location Prediction using Big Data Framework

This paper investigates with the automated classification of tweetswhich turns out to be a very complicated problem because of its nature,heterogeneity and the amount of data. According to internet live stats, nearly 500million tweets are tweeted per day, where the user’s opinion about different topicsis shared. An automated decision support system is developed to analyze thetweets related to crime against women and children. The problem is viewed in abig data perspective because of the nature of data. The proposed work focuses ondeveloping two systems: Hadoop MapReduce and Apache Spark framework forprogramming with Big Data. The algorithm based on hierarchical domain lexiconclassifies different types of crime in a parallel and distributed manner. Moreover,the crime classification tool is based on hybridized Machine Learning techniquescombined with Natural Language Processing techniques. To predict the location oftwitter users, multinomial Naive Bayes classifier trained on Location Indicativeterms and other vital parameters (such as city/country names, #hash tags and@mentions) is implemented. Our approach outperforms in terms of classificationaccuracy, mean and median error distance when compared with other algorithmsbased on parameters such as Location Indicative terms, #hash tags andcity/country names.