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 Naive 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.
Highlights
Sentiment Analysis (SA) has become one of the most active topics in information retrieval and text mining due to the large expansion of the World Wide Web
SA is the field of study that deals with automatic analysis of people‟s opinions, sentiments, appraisals, attitudes, and emotions toward entities and their attributes expressed in written text [1]
Multinomial Naïve Bayes is the most variation of NB that is mostly used in text categorization and sentiment analysis [24]
Summary
Sentiment Analysis (SA) has become one of the most active topics in information retrieval and text mining due to the large expansion of the World Wide Web. Classifying opinion text at the document level or at the sentence level as positive or negative is insufficient for most applications. These classifications do not tell what each opinion is about, that is, the target of opinion. For a more complete analysis, aspects need to be discovered before to determine whether the sentiment is positive, negative, or neutral about each aspect To obtain this level of fine-grained results, Aspect-based Sentiment Analysis (ABSA) is applied [3]. This latter considers relations between the aspects of the object of the opinion and the document polarity (positive or negative feeling expressed in the opinion). The implicit aspects (which can be indicated by adjectives, adverbs, verbs or phrasal verbs) are very important that they can convey the opinions and help in improving the performance of SA systems
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