WordNet based implicit aspect sentiment analysis for crime identification from Twitter

Abstract

Crime analysis has become an interesting field that deals with serious public safety issues recognized around the world. Today, investigating Twitter Sentiment Analysis (SA) is a continuing concern within this field. Aspect based SA, the process by which information can be extracted, analyzed and classified, is applied to tweet datasets for sentiment polarity classification to predict crimes. This paper addresses the aspect identification task involving implicit aspect implied by adjectives and verbs for crime tweets. The proposed hybrid model is based on WordNet semantic relations and Term-Weighting scheme, to enhance training data for (1) Crime Implicit Aspect sentences detection (IASD) and (2) Crime Implicit Aspect Identification (IAI). The performance is evaluated using three classifiers Multinomial Naïve Bayes, Support Vector Machine and Random Forest on three Twitter crime datasets. The obtained results demonstrate the effectiveness of WN synonym and definition relations and prove the importance of verbs in training data enhancement for crime IASD and IAI.