ArSentD-LEV: A multi-topic corpus for target-based sentiment analysis in arabic levantine tweets

作     者：Baly, Ramy Khaddaj, Alaa Hajj, Hazem El-Hajj, Wassim Shaban, Khaled Bashir 

作者机构：MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA02139 United States American University of Beirut Electrical and Computer Engineering Department Beirut Lebanon American University of Beirut Computer Science Department Beirut Lebanon Qatar University Computer Science and Engineering Department Doha Qatar 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2019年

核心收录：

主　　题：Sentiment analysis 

摘      要：Sentiment analysis is a highly subjective and challenging task. Its complexity further increases when applied to the Arabic language, mainly because of the large variety of dialects that are unstandardized and widely used in the Web, especially in social media. While many datasets have been released to train sentiment classifiers in Arabic, most of these datasets contain shallow annotation, only marking the sentiment of the text unit, as a word, a sentence or a document. In this paper, we present the Arabic Sentiment Twitter Dataset for the Levantine dialect (ArSenTD-LEV). Based on findings from analyzing tweets from the Levant region, we created a dataset of 4,000 tweets with the following annotations: The overall sentiment of the tweet, the target to which the sentiment was expressed, how the sentiment was expressed, and the topic of the tweet. Results confirm the importance of these annotations at improving the performance of a baseline sentiment classifier. They also confirm the gap of training in a certain domain, and testing in another domain. Copyright © 2019, The Authors. All rights reserved.