Work

Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach

Year: 2018

Type: preprint

Abstract: Sentiment Analysis in Arabic is a challenging task due to the rich morphology of the language. Moreover, the task is further complicated when applied to Twitter data that is known to be highly informal and noisy. In this paper, we develop a hybrid method for sentiment analysis for Arabic tweets for a specific Arabic dialect which is the Saudi Dialect. Several features were engineered and evaluated using a feature backward selection method. Then a hybrid method that combines a corpus-based and lexicon-based method was developed for several classification models (two-way, three-way, four-way). The best F1-score for each of these models was (69.9,61.63,55.07) respectively.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1805.08533

Authors: Al-Twairesh, Nora, Al-Khalifa, Hend, Alsalman, AbdulMalik, Al-Ohali, Yousef

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2804438185

Cites: 27

Cited by: 27

Related to: 10