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

KADDOURA, Sanaa, ITANI, Maher and ROAST, Chris (2021). Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text. Applied Sciences, 11 (11).

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Official URL: https://www.mdpi.com/2076-3417/11/11/4768
Open Access URL: https://www.mdpi.com/2076-3417/11/11/4768/pdf (Published version)
Link to published version:: https://doi.org/10.3390/app11114768
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    Abstract

    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.

    Item Type: Article
    Additional Information: ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 2076-3417 **History: published 22-05-2021; accepted 18-05-2021
    Uncontrolled Keywords: social networks, sentiment analysis, Arabic language, negation
    Identification Number: https://doi.org/10.3390/app11114768
    SWORD Depositor: Colin Knott
    Depositing User: Colin Knott
    Date Deposited: 26 May 2021 11:51
    Last Modified: 26 May 2021 12:00
    URI: http://shura.shu.ac.uk/id/eprint/28685

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