2019 · 142 citations · 29 references
Sentiment analysis is a widely used NLP application, yet most research and systems focus on English, leaving Arabic resources scarce. This work introduces Mazajak, an online Arabic sentiment analysis system. Mazajak employs a deep‑learning model to perform sentiment classification in an online platform. It achieves state‑of‑the‑art performance on Arabic dialect datasets such as SemEval 2017 and ASTD, and its availability can support a range of applications and research.
Sentiment analysis (SA) is one of the most useful natural language processing applications. Literature is flooding with many papers and systems addressing this task, but most of the work is focused on English. In this paper, we present "Mazajak", an online system for Arabic SA. The system is based on a deep learning model, which achieves state-of-the-art results on many Arabic dialect datasets including SemEval 2017 and ASTD. The availability of such system should assist various applications and research that rely on sentiment analysis as a tool.
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Efficient Estimation of Word Representations in Vector Space
Tomáš Mikolov, Kai Chen, Greg S. Corrado · arXiv (Cornell University) · 2013 · 18.1K citations · Full text
Efficient Estimation of Word Representations in Vector Space
Tomáš Mikolov, Kai Chen, Greg S. Corrado et al. · arXiv (Cornell University) · 2013 · 11.7K citations · Full text
Bo Pang, Lillian Lee, Shivakumar Vaithyanathan · 2002 · 7K citations · Full text
Engineering, Maximum Entropy Classification, Multimodal Sentiment Analysis +18