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Article

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

by 1,*,†, 2,† and 3,†
1
Computing and Applied Technology Department, College of Technological Innovation, Zayed University, Abu Dhabi 144534, United Arab Emirates
2
Academic Development Division, Computing Department, Sabis Educational Services, Adma 1200, Lebanon
3
Department of Computing, Sheffield Hallam University, Sheffield S1 1WB, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Barbara Guidi, Carlos A. Iglesias, Giulio Rossetti and Kevin Koidl
Appl. Sci. 2021, 11(11), 4768; https://doi.org/10.3390/app11114768
Received: 20 April 2021 / Revised: 11 May 2021 / Accepted: 18 May 2021 / Published: 22 May 2021
(This article belongs to the Special Issue Advanced Analysis Technologies for Social Media)
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. View Full-Text
Keywords: social networks; sentiment analysis; Arabic language; negation social networks; sentiment analysis; Arabic language; negation
MDPI and ACS Style

Kaddoura, S.; Itani, M.; Roast, C. Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text. Appl. Sci. 2021, 11, 4768. https://doi.org/10.3390/app11114768

AMA Style

Kaddoura S, Itani M, Roast C. Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text. Applied Sciences. 2021; 11(11):4768. https://doi.org/10.3390/app11114768

Chicago/Turabian Style

Kaddoura, Sanaa, Maher Itani, and Chris Roast. 2021. "Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text" Applied Sciences 11, no. 11: 4768. https://doi.org/10.3390/app11114768

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