Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/11/4768 |
| _version_ | 1797533022756536320 |
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| author | Sanaa Kaddoura Maher Itani Chris Roast |
| author_facet | Sanaa Kaddoura Maher Itani Chris Roast |
| author_sort | Sanaa Kaddoura |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:08:42Z |
| format | Article |
| id | doaj.art-c84c0e8677d2448290b80e67858db88d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:08:42Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c84c0e8677d2448290b80e67858db88d2023-11-21T20:57:29ZengMDPI AGApplied Sciences2076-34172021-05-011111476810.3390/app11114768Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic TextSanaa Kaddoura0Maher Itani1Chris Roast2Computing and Applied Technology Department, College of Technological Innovation, Zayed University, Abu Dhabi 144534, United Arab EmiratesAcademic Development Division, Computing Department, Sabis Educational Services, Adma 1200, LebanonDepartment of Computing, Sheffield Hallam University, Sheffield S1 1WB, UKWith 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.https://www.mdpi.com/2076-3417/11/11/4768social networkssentiment analysisArabic languagenegation |
| spellingShingle | Sanaa Kaddoura Maher Itani Chris Roast Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text Applied Sciences social networks sentiment analysis Arabic language negation |
| title | Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text |
| title_full | Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text |
| title_fullStr | Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text |
| title_full_unstemmed | Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text |
| title_short | Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text |
| title_sort | analyzing the effect of negation in sentiment polarity of facebook dialectal arabic text |
| topic | social networks sentiment analysis Arabic language negation |
| url | https://www.mdpi.com/2076-3417/11/11/4768 |
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