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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

WordNet based Implicit Aspect Sentiment Analysis for Crime Identification from Twitter

Author 1: Hajar El Hannach
Author 2: Mohammed Benkhalifa

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.091222

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 12, 2018.

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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.

Keywords: Implicit aspect based sentiment analysis; information retrieval; machine learning; supervised approaches; frequency model; WordNet; crime detection; hate crime twitter sentiment (HCTS)

Hajar El Hannach and Mohammed Benkhalifa, “WordNet based Implicit Aspect Sentiment Analysis for Crime Identification from Twitter” International Journal of Advanced Computer Science and Applications(IJACSA), 9(12), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091222

@article{Hannach2018,
title = {WordNet based Implicit Aspect Sentiment Analysis for Crime Identification from Twitter},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091222},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091222},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {12},
author = {Hajar El Hannach and Mohammed Benkhalifa}
}


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