Arabic Sentiment Analysis Problems and Challenges

Zahidi, Youssra; Younoussi, Yacine El; Al-Amrani, Yassine

doi:10.1109/jicv51605.2020.9375650

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Many general systematic survey papers have been published recently that surveyed ASA in general, such as [6][7][8][9], which covered different aspects related to ASA. Researchers in [10,11] discussed the challenges and issues facing ASA. In [12][13][14][15], the authors reviewed the tools and approaches of ASA available in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Hadwan¹,

Al-Hagery²,

Al-Sarem³

et al. 2022

Computers, Materials &Amp; Continua

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Different types of pandemics that have appeared from time to time have changed many aspects of daily life. Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown. The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store. A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications. The manual processing and extracting of information from users' reviews is an extremely difficult and time-consuming task. Therefore, the use of intelligent methods is necessary to analyse users' reviews and extract issues that can help in improving these apps. This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning methods. To the best of our knowledge, this is the first study to explore users' opinions about governmental apps in Saudi Arabia. The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases. A new dataset used for this research includes 8000 user reviews gathered from social media, Google Play and the app store. Different methods are applied to the dataset, and the results show that the k nearest neighbourhood (KNN) method generates the highest accuracy compared to other implemented methods.

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Section: Literature Reviewmentioning

confidence: 99%

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Hadwan¹,

Al-Hagery²,

Al-Sarem³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

Comparative Analysis of Machine Learning Algorithms for Arabic Sentiment Analysis on Imbalanced Social Media Data

Almuhaya,

Saha,

Kaur

et al. 2024

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)

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Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text

Kaddoura

Itani²,

Roast

2021

Applied Sciences

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With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors’ opinions about specific subjects, such as people’s opinions towards steps taken to manage the COVID-19 pandemic. Usually, people use dialectal language in their posts on social networks. Dialectal language has obstacles that make opinion analysis a challenging process compared to working with standard language. For the Arabic language, Modern Standard Arabic tools (MSA) cannot be employed with social network data that contain dialectal language. Another challenge of the dialectal Arabic language is the polarity of opinionated words affected by inverters, such as negation, that tend to change the word’s polarity from positive to negative and vice versa. This work analyzes the effect of inverters on sentiment analysis of social network dialectal Arabic posts. It discusses the different reasons that hinder the trivial resolution of inverters. An experiment is conducted on a corpus of data collected from Facebook. However, the same work can be applied to other social network posts. The results show the impact that resolution of negation may have on the classification accuracy. The results show that the F1 score increases by 20% if negation is treated in the text.

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Arabic Sentiment Analysis Problems and Challenges

Cited by 3 publications

References 10 publications

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Comparative Analysis of Machine Learning Algorithms for Arabic Sentiment Analysis on Imbalanced Social Media Data

Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text

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