Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages Article Swipe
Muhammad Kashif Nazir
,
C. M. Nadeem Faisal
,
Muhammad Asif Habib
,
Haseeb Ahmad
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3527710
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3527710
The widespread use of online social media has enabled users to express their thoughts, feelings, opinions, and sentiments in their preferred languages. These diverse perspectives offer valuable insights for data-driven decision-making. While extensive sentiment analysis approaches have been developed for resource-rich languages like English and Chinese, low-resource languages such as Roman Urdu and Roman Punjabi, especially in code-mixed contexts, have been largely neglected due to the lack of datasets and limited research on their unique morphological structures and grammatical complexities. This study aims to present a novel approach for multiclass sentiment analysis of low-resource, code-mixed datasets using multilingual transformers. Specifically, a dataset comprising Roman Urdu, Roman Punjabi, and English comments was collected. After applying traditional natural language preprocessing techniques, transformer-based libraries were used for tokenization and embedding. Subsequently, the Multilingual Bidirectional Encoder Representations from Transformers (mBERT) model was optimized and trained for multiclass sentiment analysis on the code-mixed data. The evaluation results showed a significant improvement in accuracy (+22.55%), precision (+21.06%), recall (+22.55%), and F-measure (+25.50%) compared to benchmark algorithms. Additionally, the proposed model outperformed other transformer-based models, as well as deep learning and machine learning algorithms in sentiment extraction from code-mixed data. These findings highlight the potential of the proposed approach for sentiment analysis in low-resource, code-mixed languages.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3527710
- OA Status
- gold
- Cited By
- 9
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406202058
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406202058Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2025.3527710Digital Object Identifier
- Title
-
Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource LanguagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-01Full publication date if available
- Authors
-
Muhammad Kashif Nazir, C. M. Nadeem Faisal, Muhammad Asif Habib, Haseeb AhmadList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2025.3527710Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2025.3527710Direct OA link when available
- Concepts
-
Computer science, Natural language processing, Sentiment analysis, Artificial intelligence, Information retrievalTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9Per-year citation counts (last 5 years)
- References (count)
-
58Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4406202058 |
|---|---|
| doi | https://doi.org/10.1109/access.2025.3527710 |
| ids.doi | https://doi.org/10.1109/access.2025.3527710 |
| ids.openalex | https://openalex.org/W4406202058 |
| fwci | 43.37770632 |
| type | article |
| title | Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages |
| biblio.issue | |
| biblio.volume | 13 |
| biblio.last_page | 7554 |
| biblio.first_page | 7538 |
| topics[0].id | https://openalex.org/T10181 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9714999794960022 |
| 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 | Natural Language Processing Techniques |
| 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 | 0.9386000037193298 |
| 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/T10201 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9380999803543091 |
| 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 | Speech Recognition and Synthesis |
| is_xpac | False |
| apc_list.value | 1850 |
| apc_list.currency | USD |
| apc_list.value_usd | 1850 |
| apc_paid.value | 1850 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1850 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8371220827102661 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C204321447 |
| concepts[1].level | 1 |
| concepts[1].score | 0.4666215181350708 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[1].display_name | Natural language processing |
| concepts[2].id | https://openalex.org/C66402592 |
| concepts[2].level | 2 |
| concepts[2].score | 0.41215139627456665 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2271421 |
| concepts[2].display_name | Sentiment analysis |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.40522050857543945 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C23123220 |
| concepts[4].level | 1 |
| concepts[4].score | 0.34210628271102905 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q816826 |
| concepts[4].display_name | Information retrieval |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8371220827102661 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/natural-language-processing |
| keywords[1].score | 0.4666215181350708 |
| keywords[1].display_name | Natural language processing |
| keywords[2].id | https://openalex.org/keywords/sentiment-analysis |
| keywords[2].score | 0.41215139627456665 |
| keywords[2].display_name | Sentiment analysis |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.40522050857543945 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/information-retrieval |
| keywords[4].score | 0.34210628271102905 |
| keywords[4].display_name | Information retrieval |
| language | en |
| locations[0].id | doi:10.1109/access.2025.3527710 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2485537415 |
| locations[0].source.issn | 2169-3536 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2169-3536 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | IEEE Access |
| locations[0].source.host_organization | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_name | Institute of Electrical and Electronics Engineers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | IEEE Access |
| locations[0].landing_page_url | https://doi.org/10.1109/access.2025.3527710 |
| locations[1].id | pmh:oai:doaj.org/article:fbca1f06d25043338f651940cc50f4af |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | IEEE Access, Vol 13, Pp 7538-7554 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/fbca1f06d25043338f651940cc50f4af |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5019039502 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4094-4412 |
| authorships[0].author.display_name | Muhammad Kashif Nazir |
| authorships[0].countries | PK |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I505182 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Computer Science, National Textile University, Faisalabad, Pakistan |
| authorships[0].institutions[0].id | https://openalex.org/I505182 |
| authorships[0].institutions[0].ror | https://ror.org/030dak672 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I505182 |
| authorships[0].institutions[0].country_code | PK |
| authorships[0].institutions[0].display_name | National Textile University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Muhammad Kashif Nazir |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Computer Science, National Textile University, Faisalabad, Pakistan |
| authorships[1].author.id | https://openalex.org/A5032392899 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8781-4143 |
| authorships[1].author.display_name | C. M. Nadeem Faisal |
| authorships[1].countries | PK |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I505182 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer Science, National Textile University, Faisalabad, Pakistan |
| authorships[1].institutions[0].id | https://openalex.org/I505182 |
| authorships[1].institutions[0].ror | https://ror.org/030dak672 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I505182 |
| authorships[1].institutions[0].country_code | PK |
| authorships[1].institutions[0].display_name | National Textile University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | CM Nadeem Faisal |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer Science, National Textile University, Faisalabad, Pakistan |
| authorships[2].author.id | https://openalex.org/A5089348789 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2675-1975 |
| authorships[2].author.display_name | Muhammad Asif Habib |
| authorships[2].countries | PK |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I505182 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Computer Science, National Textile University, Faisalabad, Pakistan |
| authorships[2].institutions[0].id | https://openalex.org/I505182 |
| authorships[2].institutions[0].ror | https://ror.org/030dak672 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I505182 |
| authorships[2].institutions[0].country_code | PK |
| authorships[2].institutions[0].display_name | National Textile University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Muhammad Asif Habib |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Computer Science, National Textile University, Faisalabad, Pakistan |
| authorships[3].author.id | https://openalex.org/A5076870712 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6359-7452 |
| authorships[3].author.display_name | Haseeb Ahmad |
| authorships[3].countries | PK |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I505182 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Computer Science, National Textile University, Faisalabad, Pakistan |
| authorships[3].institutions[0].id | https://openalex.org/I505182 |
| authorships[3].institutions[0].ror | https://ror.org/030dak672 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I505182 |
| authorships[3].institutions[0].country_code | PK |
| authorships[3].institutions[0].display_name | National Textile University |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Haseeb Ahmad |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Computer Science, National Textile University, Faisalabad, Pakistan |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1109/access.2025.3527710 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10181 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9714999794960022 |
| 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 | Natural Language Processing Techniques |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W2548633793, https://openalex.org/W3013279174, https://openalex.org/W2941935829, https://openalex.org/W2596247554, https://openalex.org/W3132372214, https://openalex.org/W4224284088, https://openalex.org/W3204019825 |
| cited_by_count | 9 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 9 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1109/access.2025.3527710 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2485537415 |
| best_oa_location.source.issn | 2169-3536 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2169-3536 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | IEEE Access |
| best_oa_location.source.host_organization | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | IEEE Access |
| best_oa_location.landing_page_url | https://doi.org/10.1109/access.2025.3527710 |
| primary_location.id | doi:10.1109/access.2025.3527710 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2485537415 |
| primary_location.source.issn | 2169-3536 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2169-3536 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | IEEE Access |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Access |
| primary_location.landing_page_url | https://doi.org/10.1109/access.2025.3527710 |
| publication_date | 2025-01-01 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4393357787, https://openalex.org/W3209748213, https://openalex.org/W4281692210, https://openalex.org/W4399995119, https://openalex.org/W4381569739, https://openalex.org/W4317930845, https://openalex.org/W4289861361, https://openalex.org/W2606066802, https://openalex.org/W4402510813, https://openalex.org/W4289936799, https://openalex.org/W4381592361, https://openalex.org/W3035232373, https://openalex.org/W2954536051, https://openalex.org/W3131290958, https://openalex.org/W4394807459, https://openalex.org/W4205973286, https://openalex.org/W3147144921, https://openalex.org/W3139112046, https://openalex.org/W4281783552, https://openalex.org/W4225012186, https://openalex.org/W4378530943, https://openalex.org/W6869756619, https://openalex.org/W4382680318, https://openalex.org/W4311457682, https://openalex.org/W2148506018, https://openalex.org/W3123967386, https://openalex.org/W3046194256, https://openalex.org/W3122657203, https://openalex.org/W6682729892, https://openalex.org/W2019268418, https://openalex.org/W3043883491, https://openalex.org/W3021774969, https://openalex.org/W2902155814, https://openalex.org/W2796452002, https://openalex.org/W6754035475, https://openalex.org/W2398186482, https://openalex.org/W4393054218, https://openalex.org/W4404207429, https://openalex.org/W4393170826, https://openalex.org/W3014558611, https://openalex.org/W3039554467, https://openalex.org/W3046029109, https://openalex.org/W6849585315, https://openalex.org/W4365514471, https://openalex.org/W4387350604, https://openalex.org/W4390860716, https://openalex.org/W4385573024, https://openalex.org/W4210843599, https://openalex.org/W2836242602, https://openalex.org/W2985783697, https://openalex.org/W3005825976, https://openalex.org/W2203890649, https://openalex.org/W3128858021, https://openalex.org/W4402728114, https://openalex.org/W4391554431, https://openalex.org/W6755207826, https://openalex.org/W4225906996, https://openalex.org/W4319010160 |
| referenced_works_count | 58 |
| abstract_inverted_index.a | 85, 100, 153 |
| abstract_inverted_index.as | 49, 178, 180 |
| abstract_inverted_index.in | 18, 56, 156, 187, 205 |
| abstract_inverted_index.of | 3, 67, 92, 198 |
| abstract_inverted_index.on | 72, 145 |
| abstract_inverted_index.to | 10, 64, 83, 167 |
| abstract_inverted_index.The | 0, 149 |
| abstract_inverted_index.and | 16, 44, 52, 69, 77, 107, 125, 139, 163, 183 |
| abstract_inverted_index.due | 63 |
| abstract_inverted_index.for | 28, 39, 88, 123, 141, 202 |
| abstract_inverted_index.has | 7 |
| abstract_inverted_index.the | 65, 128, 146, 171, 196, 199 |
| abstract_inverted_index.use | 2 |
| abstract_inverted_index.was | 110, 137 |
| abstract_inverted_index.This | 80 |
| abstract_inverted_index.Urdu | 51 |
| abstract_inverted_index.aims | 82 |
| abstract_inverted_index.been | 37, 60 |
| abstract_inverted_index.deep | 181 |
| abstract_inverted_index.from | 133, 190 |
| abstract_inverted_index.have | 36, 59 |
| abstract_inverted_index.lack | 66 |
| abstract_inverted_index.like | 42 |
| abstract_inverted_index.such | 48 |
| abstract_inverted_index.used | 122 |
| abstract_inverted_index.well | 179 |
| abstract_inverted_index.were | 121 |
| abstract_inverted_index.After | 112 |
| abstract_inverted_index.Roman | 50, 53, 103, 105 |
| abstract_inverted_index.These | 22, 193 |
| abstract_inverted_index.Urdu, | 104 |
| abstract_inverted_index.While | 31 |
| abstract_inverted_index.data. | 148, 192 |
| abstract_inverted_index.media | 6 |
| abstract_inverted_index.model | 136, 173 |
| abstract_inverted_index.novel | 86 |
| abstract_inverted_index.offer | 25 |
| abstract_inverted_index.other | 175 |
| abstract_inverted_index.study | 81 |
| abstract_inverted_index.their | 12, 19, 73 |
| abstract_inverted_index.users | 9 |
| abstract_inverted_index.using | 96 |
| abstract_inverted_index.online | 4 |
| abstract_inverted_index.recall | 161 |
| abstract_inverted_index.showed | 152 |
| abstract_inverted_index.social | 5 |
| abstract_inverted_index.unique | 74 |
| abstract_inverted_index.(mBERT) | 135 |
| abstract_inverted_index.Encoder | 131 |
| abstract_inverted_index.English | 43, 108 |
| abstract_inverted_index.dataset | 101 |
| abstract_inverted_index.diverse | 23 |
| abstract_inverted_index.enabled | 8 |
| abstract_inverted_index.express | 11 |
| abstract_inverted_index.largely | 61 |
| abstract_inverted_index.limited | 70 |
| abstract_inverted_index.machine | 184 |
| abstract_inverted_index.models, | 177 |
| abstract_inverted_index.natural | 115 |
| abstract_inverted_index.present | 84 |
| abstract_inverted_index.results | 151 |
| abstract_inverted_index.trained | 140 |
| abstract_inverted_index.Chinese, | 45 |
| abstract_inverted_index.Punjabi, | 54, 106 |
| abstract_inverted_index.accuracy | 157 |
| abstract_inverted_index.analysis | 34, 91, 144, 204 |
| abstract_inverted_index.applying | 113 |
| abstract_inverted_index.approach | 87, 201 |
| abstract_inverted_index.comments | 109 |
| abstract_inverted_index.compared | 166 |
| abstract_inverted_index.datasets | 68, 95 |
| abstract_inverted_index.findings | 194 |
| abstract_inverted_index.insights | 27 |
| abstract_inverted_index.language | 116 |
| abstract_inverted_index.learning | 182, 185 |
| abstract_inverted_index.proposed | 172, 200 |
| abstract_inverted_index.research | 71 |
| abstract_inverted_index.valuable | 26 |
| abstract_inverted_index.(+25.50%) | 165 |
| abstract_inverted_index.F-measure | 164 |
| abstract_inverted_index.benchmark | 168 |
| abstract_inverted_index.contexts, | 58 |
| abstract_inverted_index.developed | 38 |
| abstract_inverted_index.extensive | 32 |
| abstract_inverted_index.feelings, | 14 |
| abstract_inverted_index.highlight | 195 |
| abstract_inverted_index.languages | 41, 47 |
| abstract_inverted_index.libraries | 120 |
| abstract_inverted_index.neglected | 62 |
| abstract_inverted_index.opinions, | 15 |
| abstract_inverted_index.optimized | 138 |
| abstract_inverted_index.potential | 197 |
| abstract_inverted_index.precision | 159 |
| abstract_inverted_index.preferred | 20 |
| abstract_inverted_index.sentiment | 33, 90, 143, 188, 203 |
| abstract_inverted_index.thoughts, | 13 |
| abstract_inverted_index.(+21.06%), | 160 |
| abstract_inverted_index.(+22.55%), | 158, 162 |
| abstract_inverted_index.algorithms | 186 |
| abstract_inverted_index.approaches | 35 |
| abstract_inverted_index.code-mixed | 57, 94, 147, 191, 207 |
| abstract_inverted_index.collected. | 111 |
| abstract_inverted_index.comprising | 102 |
| abstract_inverted_index.embedding. | 126 |
| abstract_inverted_index.especially | 55 |
| abstract_inverted_index.evaluation | 150 |
| abstract_inverted_index.extraction | 189 |
| abstract_inverted_index.languages. | 21, 208 |
| abstract_inverted_index.multiclass | 89, 142 |
| abstract_inverted_index.sentiments | 17 |
| abstract_inverted_index.structures | 76 |
| abstract_inverted_index.widespread | 1 |
| abstract_inverted_index.algorithms. | 169 |
| abstract_inverted_index.data-driven | 29 |
| abstract_inverted_index.grammatical | 78 |
| abstract_inverted_index.improvement | 155 |
| abstract_inverted_index.significant | 154 |
| abstract_inverted_index.techniques, | 118 |
| abstract_inverted_index.traditional | 114 |
| abstract_inverted_index.Multilingual | 129 |
| abstract_inverted_index.Transformers | 134 |
| abstract_inverted_index.low-resource | 46 |
| abstract_inverted_index.multilingual | 97 |
| abstract_inverted_index.outperformed | 174 |
| abstract_inverted_index.perspectives | 24 |
| abstract_inverted_index.tokenization | 124 |
| abstract_inverted_index.Additionally, | 170 |
| abstract_inverted_index.Bidirectional | 130 |
| abstract_inverted_index.Specifically, | 99 |
| abstract_inverted_index.Subsequently, | 127 |
| abstract_inverted_index.complexities. | 79 |
| abstract_inverted_index.low-resource, | 93, 206 |
| abstract_inverted_index.morphological | 75 |
| abstract_inverted_index.preprocessing | 117 |
| abstract_inverted_index.resource-rich | 40 |
| abstract_inverted_index.transformers. | 98 |
| abstract_inverted_index.Representations | 132 |
| abstract_inverted_index.decision-making. | 30 |
| abstract_inverted_index.transformer-based | 119, 176 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.99667003 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |