AraCust: a Saudi Telecom Tweets corpus for sentiment analysis
Comparing Arabic to other languages, Arabic lacks large corpora for Natural Language Processing (Assiri, Emam & Al-Dossari, 2018; Gamal et al., 2019). A number of scholars depended on translation from one language to another to construct their corpus (Rushdi-Saleh et al., 2011). This paper prese...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-05-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-510.pdf |
| _version_ | 1818939705569312768 |
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| author | Latifah Almuqren Alexandra Cristea |
| author_facet | Latifah Almuqren Alexandra Cristea |
| author_sort | Latifah Almuqren |
| collection | DOAJ |
| description | Comparing Arabic to other languages, Arabic lacks large corpora for Natural Language Processing (Assiri, Emam & Al-Dossari, 2018; Gamal et al., 2019). A number of scholars depended on translation from one language to another to construct their corpus (Rushdi-Saleh et al., 2011). This paper presents how we have constructed, cleaned, pre-processed, and annotated our 20,0000 Gold Standard Corpus (GSC) AraCust, the first Telecom GSC for Arabic Sentiment Analysis (ASA) for Dialectal Arabic (DA). AraCust contains Saudi dialect tweets, processed from a self-collected Arabic tweets dataset and has been annotated for sentiment analysis, i.e.,manually labelled (k=0.60). In addition, we have illustrated AraCust’s power, by performing an exploratory data analysis, to analyse the features that were sourced from the nature of our corpus, to assist with choosing the right ASA methods for it. To evaluate our Golden Standard corpus AraCust, we have first applied a simple experiment, using a supervised classifier, to offer benchmark outcomes for forthcoming works. In addition, we have applied the same supervised classifier on a publicly available Arabic dataset created from Twitter, ASTD (Nabil, Aly & Atiya, 2015). The result shows that our dataset AraCust outperforms the ASTD result with 91% accuracy and 89% F1avg score. The AraCust corpus will be released, together with code useful for its exploration, via GitHub as a part of this submission. |
| first_indexed | 2024-12-20T06:28:00Z |
| format | Article |
| id | doaj.art-45f0000b1e9c4080b565e70f7b091661 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-20T06:28:00Z |
| publishDate | 2021-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-45f0000b1e9c4080b565e70f7b0916612022-12-21T19:50:14ZengPeerJ Inc.PeerJ Computer Science2376-59922021-05-017e51010.7717/peerj-cs.510AraCust: a Saudi Telecom Tweets corpus for sentiment analysisLatifah Almuqren0Alexandra Cristea1Department of Computer Science, Durham University, Durham, United KingdomDepartment of Computer Science, Durham University, Durham, United KingdomComparing Arabic to other languages, Arabic lacks large corpora for Natural Language Processing (Assiri, Emam & Al-Dossari, 2018; Gamal et al., 2019). A number of scholars depended on translation from one language to another to construct their corpus (Rushdi-Saleh et al., 2011). This paper presents how we have constructed, cleaned, pre-processed, and annotated our 20,0000 Gold Standard Corpus (GSC) AraCust, the first Telecom GSC for Arabic Sentiment Analysis (ASA) for Dialectal Arabic (DA). AraCust contains Saudi dialect tweets, processed from a self-collected Arabic tweets dataset and has been annotated for sentiment analysis, i.e.,manually labelled (k=0.60). In addition, we have illustrated AraCust’s power, by performing an exploratory data analysis, to analyse the features that were sourced from the nature of our corpus, to assist with choosing the right ASA methods for it. To evaluate our Golden Standard corpus AraCust, we have first applied a simple experiment, using a supervised classifier, to offer benchmark outcomes for forthcoming works. In addition, we have applied the same supervised classifier on a publicly available Arabic dataset created from Twitter, ASTD (Nabil, Aly & Atiya, 2015). The result shows that our dataset AraCust outperforms the ASTD result with 91% accuracy and 89% F1avg score. The AraCust corpus will be released, together with code useful for its exploration, via GitHub as a part of this submission.https://peerj.com/articles/cs-510.pdfSentiment analysisArabicGold Standard CorpusSupervised approach |
| spellingShingle | Latifah Almuqren Alexandra Cristea AraCust: a Saudi Telecom Tweets corpus for sentiment analysis PeerJ Computer Science Sentiment analysis Arabic Gold Standard Corpus Supervised approach |
| title | AraCust: a Saudi Telecom Tweets corpus for sentiment analysis |
| title_full | AraCust: a Saudi Telecom Tweets corpus for sentiment analysis |
| title_fullStr | AraCust: a Saudi Telecom Tweets corpus for sentiment analysis |
| title_full_unstemmed | AraCust: a Saudi Telecom Tweets corpus for sentiment analysis |
| title_short | AraCust: a Saudi Telecom Tweets corpus for sentiment analysis |
| title_sort | aracust a saudi telecom tweets corpus for sentiment analysis |
| topic | Sentiment analysis Arabic Gold Standard Corpus Supervised approach |
| url | https://peerj.com/articles/cs-510.pdf |
| work_keys_str_mv | AT latifahalmuqren aracustasauditelecomtweetscorpusforsentimentanalysis AT alexandracristea aracustasauditelecomtweetscorpusforsentimentanalysis |