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

2018/05/22 by Nora Al-Twairesh, Hend S. Al‐Khalifa, Al-Twairesh, Nora +5 · 3 citations
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1805.08533

openalex publication_date 2018/05/22 · openalex created_date 2018/06/01 · openalex updated_date 2026/07/28

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.

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