ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets

TL;DR

This paper introduces ArSenTD-LEV, a new multi-topic Arabic Levantine Twitter dataset with detailed annotations, aimed at improving target-based sentiment analysis in dialectal Arabic social media content.

Contribution

The creation of a comprehensive 4,000-tweet dataset with multi-faceted annotations for better sentiment analysis in Arabic dialects.

Findings

Annotations improve classifier performance.

Domain gaps affect sentiment analysis accuracy.

Multi-topic annotations are valuable for nuanced sentiment detection.

Abstract

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.