@inproceedings{b45776b3c19c4edcba86b9a21c17ec0a,
title = "Using Aspect-Based Sentiment Analysis to Evaluate Arabic News Affect on Readers",
abstract = "The rapid increase in digital information has raised great challenges especially when it comes to automated content analysis. The adoption of social media as a communication channel for political views demands automated methods for posts' tone analysis, sentiment analysis, and emotional affect. This paper proposes a novel approach of using aspect-based sentiment analysis in evaluating Arabic news posts affect on readers. The approach adopts several phases of text processing, features selection, and text classification. Two widely used classifiers, namely Conditional Random Fields (CRF) and J48, are tested. Experimentation results show that J48 outperforms CRF in aspect terms extraction whereas CRF is slightly better in aspect terms polarity identification.",
author = "Mohammad Al-Smadi and Mahmoud Al-Ayyoub and Huda Al-Sarhan and Yaser Jararweh",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 8th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2015 ; Conference date: 07-12-2015 Through 10-12-2015",
year = "2015",
doi = "10.1109/UCC.2015.78",
language = "English",
series = "Proceedings - 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing, UCC 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "436--441",
editor = "Omer Rana and Rajkumar Buyya and Ioan Raicu",
booktitle = "Proceedings - 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing, UCC 2015",
address = "United States",
}