AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets Article Swipe
This paper presents our strategy to tackle the EACL WANLP-2021 Shared Task 2: Sarcasm and Sentiment Detection. One of the subtasks aims at developing a system that identifies whether a given Arabic tweet is sarcastic in nature or not, while the other aims to identify the sentiment of the Arabic tweet. We approach the task in two steps. The first step involves pre processing the provided ArSarcasm-v2 dataset by performing insertions, deletions and segmentation operations on various parts of the text. The second step involves experimenting with multiple variants of two transformer based models, AraELECTRA and AraBERT. Our final approach was ranked seventh and fourth in the Sarcasm and Sentiment Detection subtasks respectively.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.01679
- https://arxiv.org/pdf/2103.01679
- OA Status
- green
- Cited By
- 3
- References
- 29
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3135298183
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3135298183Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2103.01679Digital Object Identifier
- Title
-
AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic TweetsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-02Full publication date if available
- Authors
-
Anshul WadhawanList of authors in order
- Landing page
-
https://arxiv.org/abs/2103.01679Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2103.01679Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2103.01679Direct OA link when available
- Concepts
-
Sarcasm, Arabic, Computer science, Natural language processing, Artificial intelligence, Segmentation, Task (project management), Sentiment analysis, Machine learning, Linguistics, Irony, Management, Philosophy, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3135298183 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2103.01679 |
| ids.doi | https://doi.org/10.48550/arxiv.2103.01679 |
| ids.mag | 3135298183 |
| ids.openalex | https://openalex.org/W3135298183 |
| fwci | |
| type | preprint |
| title | AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 400 |
| biblio.first_page | 395 |
| topics[0].id | https://openalex.org/T10664 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Sentiment Analysis and Opinion Mining |
| topics[1].id | https://openalex.org/T10028 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9995999932289124 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Topic Modeling |
| topics[2].id | https://openalex.org/T13083 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9993000030517578 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Advanced Text Analysis Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2776207355 |
| concepts[0].level | 3 |
| concepts[0].score | 0.9886144399642944 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q191035 |
| concepts[0].display_name | Sarcasm |
| concepts[1].id | https://openalex.org/C96455323 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7914495468139648 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q13955 |
| concepts[1].display_name | Arabic |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.7405723333358765 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C204321447 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6571111679077148 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[3].display_name | Natural language processing |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.6525236368179321 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C89600930 |
| concepts[5].level | 2 |
| concepts[5].score | 0.649229884147644 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[5].display_name | Segmentation |
| concepts[6].id | https://openalex.org/C2780451532 |
| concepts[6].level | 2 |
| concepts[6].score | 0.6253087520599365 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q759676 |
| concepts[6].display_name | Task (project management) |
| concepts[7].id | https://openalex.org/C66402592 |
| concepts[7].level | 2 |
| concepts[7].score | 0.6036906838417053 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2271421 |
| concepts[7].display_name | Sentiment analysis |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3274405896663666 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C41895202 |
| concepts[9].level | 1 |
| concepts[9].score | 0.15879863500595093 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[9].display_name | Linguistics |
| concepts[10].id | https://openalex.org/C2779975665 |
| concepts[10].level | 2 |
| concepts[10].score | 0.06023553013801575 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q131361 |
| concepts[10].display_name | Irony |
| concepts[11].id | https://openalex.org/C187736073 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2920921 |
| concepts[11].display_name | Management |
| concepts[12].id | https://openalex.org/C138885662 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[12].display_name | Philosophy |
| concepts[13].id | https://openalex.org/C162324750 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[13].display_name | Economics |
| keywords[0].id | https://openalex.org/keywords/sarcasm |
| keywords[0].score | 0.9886144399642944 |
| keywords[0].display_name | Sarcasm |
| keywords[1].id | https://openalex.org/keywords/arabic |
| keywords[1].score | 0.7914495468139648 |
| keywords[1].display_name | Arabic |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.7405723333358765 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/natural-language-processing |
| keywords[3].score | 0.6571111679077148 |
| keywords[3].display_name | Natural language processing |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.6525236368179321 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/segmentation |
| keywords[5].score | 0.649229884147644 |
| keywords[5].display_name | Segmentation |
| keywords[6].id | https://openalex.org/keywords/task |
| keywords[6].score | 0.6253087520599365 |
| keywords[6].display_name | Task (project management) |
| keywords[7].id | https://openalex.org/keywords/sentiment-analysis |
| keywords[7].score | 0.6036906838417053 |
| keywords[7].display_name | Sentiment analysis |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.3274405896663666 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/linguistics |
| keywords[9].score | 0.15879863500595093 |
| keywords[9].display_name | Linguistics |
| keywords[10].id | https://openalex.org/keywords/irony |
| keywords[10].score | 0.06023553013801575 |
| keywords[10].display_name | Irony |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2103.01679 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2103.01679 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2103.01679 |
| locations[1].id | mag:3135298183 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | arXiv (Cornell University) |
| locations[1].landing_page_url | https://arxiv.org/pdf/2103.01679 |
| locations[2].id | doi:10.48550/arxiv.2103.01679 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400194 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | arXiv (Cornell University) |
| locations[2].source.host_organization | https://openalex.org/I205783295 |
| locations[2].source.host_organization_name | Cornell University |
| locations[2].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://doi.org/10.48550/arxiv.2103.01679 |
| locations[3].id | mag:3154953700 |
| locations[3].is_oa | False |
| locations[3].source | |
| locations[3].license | |
| locations[3].pdf_url | |
| locations[3].version | |
| locations[3].raw_type | |
| locations[3].license_id | |
| locations[3].is_accepted | False |
| locations[3].is_published | |
| locations[3].raw_source_name | |
| locations[3].landing_page_url | https://dblp.uni-trier.de/db/journals/corr/corr2103.html#abs-2103-01679 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5069978153 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Anshul Wadhawan |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Anshul Wadhawan |
| authorships[0].is_corresponding | True |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2103.01679 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10664 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Sentiment Analysis and Opinion Mining |
| related_works | https://openalex.org/W3154953700, https://openalex.org/W3155561744, https://openalex.org/W3154166606, https://openalex.org/W3088728183, https://openalex.org/W2750787240, https://openalex.org/W3154295458, https://openalex.org/W3134332923, https://openalex.org/W3156268030, https://openalex.org/W3185621189, https://openalex.org/W2116782398, https://openalex.org/W2507305386, https://openalex.org/W2294058101, https://openalex.org/W2557909129, https://openalex.org/W3007656739, https://openalex.org/W2992839908, https://openalex.org/W2804438185, https://openalex.org/W2750939699, https://openalex.org/W3134813467, https://openalex.org/W2251770468, https://openalex.org/W818778357 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2023 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2022 |
| counts_by_year[1].cited_by_count | 2 |
| locations_count | 4 |
| best_oa_location.id | pmh:oai:arXiv.org:2103.01679 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2103.01679 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2103.01679 |
| primary_location.id | pmh:oai:arXiv.org:2103.01679 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2103.01679 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2103.01679 |
| publication_date | 2021-03-02 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2510141903, https://openalex.org/W2250480277, https://openalex.org/W3030258237, https://openalex.org/W3155561744, https://openalex.org/W1610356397, https://openalex.org/W2251920663, https://openalex.org/W2108646579, https://openalex.org/W3194034633, https://openalex.org/W2143612262, https://openalex.org/W2252381721, https://openalex.org/W2024011160, https://openalex.org/W2124752409, https://openalex.org/W3037422790, https://openalex.org/W2401379394, https://openalex.org/W2964126051, https://openalex.org/W2513973860, https://openalex.org/W2132339004, https://openalex.org/W2251379416, https://openalex.org/W2250204095, https://openalex.org/W2471147443, https://openalex.org/W3128029819, https://openalex.org/W2163605009, https://openalex.org/W3088592174, https://openalex.org/W3088728183, https://openalex.org/W2024244759, https://openalex.org/W2767481019, https://openalex.org/W1513313272, https://openalex.org/W3000128329, https://openalex.org/W2153579005 |
| referenced_works_count | 29 |
| abstract_inverted_index.a | 24, 29 |
| abstract_inverted_index.2: | 12 |
| abstract_inverted_index.We | 51 |
| abstract_inverted_index.at | 22 |
| abstract_inverted_index.by | 68 |
| abstract_inverted_index.in | 35, 55, 105 |
| abstract_inverted_index.is | 33 |
| abstract_inverted_index.of | 18, 47, 78, 89 |
| abstract_inverted_index.on | 75 |
| abstract_inverted_index.or | 37 |
| abstract_inverted_index.to | 5, 43 |
| abstract_inverted_index.One | 17 |
| abstract_inverted_index.Our | 97 |
| abstract_inverted_index.The | 58, 81 |
| abstract_inverted_index.and | 14, 72, 95, 103, 108 |
| abstract_inverted_index.our | 3 |
| abstract_inverted_index.pre | 62 |
| abstract_inverted_index.the | 7, 19, 40, 45, 48, 53, 64, 79, 106 |
| abstract_inverted_index.two | 56, 90 |
| abstract_inverted_index.was | 100 |
| abstract_inverted_index.EACL | 8 |
| abstract_inverted_index.Task | 11 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.aims | 21, 42 |
| abstract_inverted_index.not, | 38 |
| abstract_inverted_index.step | 60, 83 |
| abstract_inverted_index.task | 54 |
| abstract_inverted_index.that | 26 |
| abstract_inverted_index.with | 86 |
| abstract_inverted_index.based | 92 |
| abstract_inverted_index.final | 98 |
| abstract_inverted_index.first | 59 |
| abstract_inverted_index.given | 30 |
| abstract_inverted_index.other | 41 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.parts | 77 |
| abstract_inverted_index.text. | 80 |
| abstract_inverted_index.tweet | 32 |
| abstract_inverted_index.while | 39 |
| abstract_inverted_index.Arabic | 31, 49 |
| abstract_inverted_index.Shared | 10 |
| abstract_inverted_index.fourth | 104 |
| abstract_inverted_index.nature | 36 |
| abstract_inverted_index.ranked | 101 |
| abstract_inverted_index.second | 82 |
| abstract_inverted_index.steps. | 57 |
| abstract_inverted_index.system | 25 |
| abstract_inverted_index.tackle | 6 |
| abstract_inverted_index.tweet. | 50 |
| abstract_inverted_index.Sarcasm | 13, 107 |
| abstract_inverted_index.dataset | 67 |
| abstract_inverted_index.models, | 93 |
| abstract_inverted_index.seventh | 102 |
| abstract_inverted_index.various | 76 |
| abstract_inverted_index.whether | 28 |
| abstract_inverted_index.AraBERT. | 96 |
| abstract_inverted_index.approach | 52, 99 |
| abstract_inverted_index.identify | 44 |
| abstract_inverted_index.involves | 61, 84 |
| abstract_inverted_index.multiple | 87 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.provided | 65 |
| abstract_inverted_index.strategy | 4 |
| abstract_inverted_index.subtasks | 20, 111 |
| abstract_inverted_index.variants | 88 |
| abstract_inverted_index.Detection | 110 |
| abstract_inverted_index.Sentiment | 15, 109 |
| abstract_inverted_index.deletions | 71 |
| abstract_inverted_index.sarcastic | 34 |
| abstract_inverted_index.sentiment | 46 |
| abstract_inverted_index.AraELECTRA | 94 |
| abstract_inverted_index.Detection. | 16 |
| abstract_inverted_index.WANLP-2021 | 9 |
| abstract_inverted_index.developing | 23 |
| abstract_inverted_index.identifies | 27 |
| abstract_inverted_index.operations | 74 |
| abstract_inverted_index.performing | 69 |
| abstract_inverted_index.processing | 63 |
| abstract_inverted_index.insertions, | 70 |
| abstract_inverted_index.transformer | 91 |
| abstract_inverted_index.ArSarcasm-v2 | 66 |
| abstract_inverted_index.segmentation | 73 |
| abstract_inverted_index.experimenting | 85 |
| abstract_inverted_index.respectively. | 112 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5069978153 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile |