Sentiment analysis (SA) has been, and is still, a thriving research area.
However, the task of Arabic sentiment analysis (ASA) is still underrepresented
in the body of research. This study offers the first in-depth and in-breadth
analysis of existing ASA studies of textual content and identifies their common
themes, domains of application, methods, approaches, technologies and
algorithms used. The in-depth study manually analyses 133 ASA papers published
in the English language between 2002 and 2020 from four academic databases
(SAGE, IEEE, Springer, WILEY) and from Google Scholar. The in-breadth study
uses modern, automatic machine learning techniques, such as topic modelling and
temporal analysis, on Open Access resources, to reinforce themes and trends
identified by the prior study, on 2297 ASA publications between 2010-2020. The
main findings show the different approaches used for ASA: machine learning,
lexicon-based and hybrid approaches. Other findings include ASA 'winning'
algorithms (SVM, NB, hybrid methods). Deep learning methods, such as LSTM can
provide higher accuracy, but for ASA sometimes the corpora are not large enough
to support them. Additionally, whilst there are some ASA corpora and lexicons,
more are required. Specifically, Arabic tweets corpora and datasets are
currently only moderately sized. Moreover, Arabic lexicons that have high
coverage contain only Modern Standard Arabic (MSA) words, and those with Arabic
dialects are quite small. Thus, new corpora need to be created. On the other
hand, ASA tools are stringently lacking. There is a need to develop ASA tools
that can be used in industry, as well as in academia, for Arabic text SA.
Hence, our study offers insights into the challenges associated with ASA
research and provides suggestions for ways to move the field forward such as
lack of Dialectical Arabic resource, Arabic tweets, corpora and data sets for
SA.

通过对已发表的 133 篇英文 ASA 论文的深入研究和 2297 篇 ASA 出版物的广泛研究，本文揭示了 ASA 的常见主题、应用领域、方法、技术和算法，并强调了 ASA 所面临的挑战和未来发展方向。