Using Aspect-Based Sentiment Analysis to Evaluate Arabic News Affect on Readers

被引：17

作者：

AL-Smadi, Mohammed ^{[1
]}

Al-Ayyoub, Mahmoud ^{[1
]}

Al-Sarhan, Huda ^{[1
]}

Jararweh, Yaser ^{[1
]}

机构：

[1] Jordan Univ Sci & Technol, Irbid, Jordan

来源：

2015 IEEE/ACM 8TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC) | 2015年

关键词：

MEDIA;

D O I：

10.1109/UCC.2015.78

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：436 / 441

页数：6