Semantic Analysis Using Deep Learning for Predicting Stock Trends Article Swipe
Manisha Galphade
,
V. B. Nikam
,
Dhanalekshmi Prasad Yedurkar
,
Prabhishek Singh
,
Thompson Stephan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2024.04.078
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2024.04.078
Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2024.04.078
- OA Status
- diamond
- Cited By
- 3
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399198986
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4399198986Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.procs.2024.04.078Digital Object Identifier
- Title
-
Semantic Analysis Using Deep Learning for Predicting Stock TrendsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Manisha Galphade, V. B. Nikam, Dhanalekshmi Prasad Yedurkar, Prabhishek Singh, Thompson StephanList of authors in order
- Landing page
-
https://doi.org/10.1016/j.procs.2024.04.078Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.procs.2024.04.078Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Deep learning, Machine learning, Natural language processingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4399198986 |
|---|---|
| doi | https://doi.org/10.1016/j.procs.2024.04.078 |
| ids.doi | https://doi.org/10.1016/j.procs.2024.04.078 |
| ids.openalex | https://openalex.org/W4399198986 |
| fwci | 2.86956809 |
| type | article |
| title | Semantic Analysis Using Deep Learning for Predicting Stock Trends |
| biblio.issue | |
| biblio.volume | 235 |
| biblio.last_page | 829 |
| biblio.first_page | 820 |
| topics[0].id | https://openalex.org/T11326 |
| topics[0].field.id | https://openalex.org/fields/18 |
| topics[0].field.display_name | Decision Sciences |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1803 |
| topics[0].subfield.display_name | Management Science and Operations Research |
| topics[0].display_name | Stock Market Forecasting Methods |
| topics[1].id | https://openalex.org/T11918 |
| topics[1].field.id | https://openalex.org/fields/18 |
| topics[1].field.display_name | Decision Sciences |
| topics[1].score | 0.9879999756813049 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1803 |
| topics[1].subfield.display_name | Management Science and Operations Research |
| topics[1].display_name | Forecasting Techniques and Applications |
| topics[2].id | https://openalex.org/T12205 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9700999855995178 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1711 |
| topics[2].subfield.display_name | Signal Processing |
| topics[2].display_name | Time Series Analysis and Forecasting |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.9266178607940674 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.5647582411766052 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C108583219 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5102899074554443 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[2].display_name | Deep learning |
| concepts[3].id | https://openalex.org/C119857082 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4375301003456116 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C204321447 |
| concepts[4].level | 1 |
| concepts[4].score | 0.41985562443733215 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[4].display_name | Natural language processing |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.9266178607940674 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.5647582411766052 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/deep-learning |
| keywords[2].score | 0.5102899074554443 |
| keywords[2].display_name | Deep learning |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.4375301003456116 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/natural-language-processing |
| keywords[4].score | 0.41985562443733215 |
| keywords[4].display_name | Natural language processing |
| language | en |
| locations[0].id | doi:10.1016/j.procs.2024.04.078 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S120348307 |
| locations[0].source.issn | 1877-0509 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1877-0509 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Procedia Computer Science |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Procedia Computer Science |
| locations[0].landing_page_url | https://doi.org/10.1016/j.procs.2024.04.078 |
| locations[1].id | pmh:oai:uni-augsburg.opus-bayern.de:119016 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306400930 |
| 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 | OPUS (Augsburg University) |
| locations[1].source.host_organization | https://openalex.org/I119916105 |
| locations[1].source.host_organization_name | Augsburg University |
| locations[1].source.host_organization_lineage | https://openalex.org/I119916105 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | doc-type:article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/119016 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5006495026 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Manisha Galphade |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Manisha Galphade |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5056468192 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5809-8667 |
| authorships[1].author.display_name | V. B. Nikam |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | V.B. Nikam |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5055536860 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2235-1536 |
| authorships[2].author.display_name | Dhanalekshmi Prasad Yedurkar |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dhanalekshmi Yedurkar |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5031271088 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-9338-0932 |
| authorships[3].author.display_name | Prabhishek Singh |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Prabhishek Singh |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5048113544 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-6578-6919 |
| authorships[4].author.display_name | Thompson Stephan |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Thompson Stephan |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.procs.2024.04.078 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Semantic Analysis Using Deep Learning for Predicting Stock Trends |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11326 |
| primary_topic.field.id | https://openalex.org/fields/18 |
| primary_topic.field.display_name | Decision Sciences |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1803 |
| primary_topic.subfield.display_name | Management Science and Operations Research |
| primary_topic.display_name | Stock Market Forecasting Methods |
| related_works | https://openalex.org/W2731899572, https://openalex.org/W2961085424, https://openalex.org/W3215138031, https://openalex.org/W4306674287, https://openalex.org/W3009238340, https://openalex.org/W4321369474, https://openalex.org/W4360585206, https://openalex.org/W4285208911, https://openalex.org/W3046775127, https://openalex.org/W3082895349 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1016/j.procs.2024.04.078 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S120348307 |
| best_oa_location.source.issn | 1877-0509 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1877-0509 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Procedia Computer Science |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.license | cc-by-nc-nd |
| 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-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Procedia Computer Science |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.procs.2024.04.078 |
| primary_location.id | doi:10.1016/j.procs.2024.04.078 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S120348307 |
| primary_location.source.issn | 1877-0509 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1877-0509 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Procedia Computer Science |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Procedia Computer Science |
| primary_location.landing_page_url | https://doi.org/10.1016/j.procs.2024.04.078 |
| publication_date | 2024-01-01 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W6842607561, https://openalex.org/W6773523238, https://openalex.org/W2518345121, https://openalex.org/W6643921026, https://openalex.org/W6747733823, https://openalex.org/W2969519670, https://openalex.org/W2979835384, https://openalex.org/W6789834888, https://openalex.org/W6772202981, https://openalex.org/W2891929938, https://openalex.org/W2911711641, https://openalex.org/W4327969336, https://openalex.org/W3217550500, https://openalex.org/W4385196301, https://openalex.org/W6823237816, https://openalex.org/W2190007662, https://openalex.org/W6789508523, https://openalex.org/W6689015942, https://openalex.org/W4200065055, https://openalex.org/W2981537679, https://openalex.org/W6772933592, https://openalex.org/W6783782693, https://openalex.org/W4289792687, https://openalex.org/W2162359284, https://openalex.org/W3212007445, https://openalex.org/W6767182473, https://openalex.org/W4247145904, https://openalex.org/W4239091267, https://openalex.org/W4241018589, https://openalex.org/W4243523213, https://openalex.org/W4300485781, https://openalex.org/W4240629480, https://openalex.org/W4206357429, https://openalex.org/W2243598854, https://openalex.org/W4231080853, https://openalex.org/W4241071796 |
| referenced_works_count | 36 |
| abstract_inverted_index.a | 106, 135, 161, 175, 233, 281 |
| abstract_inverted_index.In | 92, 171 |
| abstract_inverted_index.It | 78, 265 |
| abstract_inverted_index.an | 30, 272 |
| abstract_inverted_index.as | 39, 41 |
| abstract_inverted_index.be | 80 |
| abstract_inverted_index.if | 121 |
| abstract_inverted_index.in | 18, 56, 71, 105, 139, 186 |
| abstract_inverted_index.is | 134, 158, 266 |
| abstract_inverted_index.of | 33, 49, 69, 88, 103, 130, 137, 163, 207, 212, 274 |
| abstract_inverted_index.on | 7, 178, 276 |
| abstract_inverted_index.to | 10, 16, 85, 108, 118, 182, 222 |
| abstract_inverted_index.we | 95 |
| abstract_inverted_index.The | 148, 190, 205, 236, 251 |
| abstract_inverted_index.and | 2, 20, 45, 83, 115, 197, 209 |
| abstract_inverted_index.any | 72, 125 |
| abstract_inverted_index.are | 59, 68, 253 |
| abstract_inverted_index.but | 62 |
| abstract_inverted_index.for | 75, 143, 160, 248 |
| abstract_inverted_index.its | 42, 50 |
| abstract_inverted_index.out | 120 |
| abstract_inverted_index.the | 14, 22, 34, 46, 101, 116, 128, 140, 179, 187, 226, 230, 241, 245, 261, 269 |
| abstract_inverted_index.way | 117 |
| abstract_inverted_index.BERT | 180 |
| abstract_inverted_index.RNN. | 218 |
| abstract_inverted_index.This | 219 |
| abstract_inverted_index.aids | 221 |
| abstract_inverted_index.also | 254 |
| abstract_inverted_index.both | 81 |
| abstract_inverted_index.data | 114 |
| abstract_inverted_index.from | 112 |
| abstract_inverted_index.help | 11 |
| abstract_inverted_index.lack | 136 |
| abstract_inverted_index.past | 37 |
| abstract_inverted_index.rely | 6 |
| abstract_inverted_index.same | 262 |
| abstract_inverted_index.set. | 284 |
| abstract_inverted_index.such | 122 |
| abstract_inverted_index.text | 54 |
| abstract_inverted_index.that | 67, 157, 240, 268 |
| abstract_inverted_index.them | 12 |
| abstract_inverted_index.they | 63 |
| abstract_inverted_index.this | 93, 172 |
| abstract_inverted_index.used | 159 |
| abstract_inverted_index.uses | 193 |
| abstract_inverted_index.very | 153 |
| abstract_inverted_index.ways | 15 |
| abstract_inverted_index.well | 40 |
| abstract_inverted_index.will | 79 |
| abstract_inverted_index.with | 100, 201, 217, 256 |
| abstract_inverted_index.work | 119, 192 |
| abstract_inverted_index.There | 133 |
| abstract_inverted_index.along | 216 |
| abstract_inverted_index.built | 177 |
| abstract_inverted_index.data. | 91 |
| abstract_inverted_index.hints | 126 |
| abstract_inverted_index.model | 151, 176 |
| abstract_inverted_index.offer | 29 |
| abstract_inverted_index.other | 257 |
| abstract_inverted_index.shows | 239 |
| abstract_inverted_index.stake | 76 |
| abstract_inverted_index.using | 260, 280 |
| abstract_inverted_index.value | 48 |
| abstract_inverted_index.words | 231 |
| abstract_inverted_index.84.77% | 275 |
| abstract_inverted_index.assess | 21 |
| abstract_inverted_index.decide | 13 |
| abstract_inverted_index.design | 206 |
| abstract_inverted_index.domain | 164 |
| abstract_inverted_index.invest | 17 |
| abstract_inverted_index.layers | 196, 200, 215 |
| abstract_inverted_index.lesser | 282 |
| abstract_inverted_index.little | 154 |
| abstract_inverted_index.market | 47, 51 |
| abstract_inverted_index.method | 107, 243 |
| abstract_inverted_index.models | 259 |
| abstract_inverted_index.mostly | 5 |
| abstract_inverted_index.paper, | 173 |
| abstract_inverted_index.report | 58 |
| abstract_inverted_index.share. | 52 |
| abstract_inverted_index.stocks | 19 |
| abstract_inverted_index.study, | 94 |
| abstract_inverted_index.trends | 129 |
| abstract_inverted_index.twelve | 194, 198 |
| abstract_inverted_index.useful | 110 |
| abstract_inverted_index.within | 232 |
| abstract_inverted_index.Company | 0 |
| abstract_inverted_index.FinBERT | 270 |
| abstract_inverted_index.address | 96, 183 |
| abstract_inverted_index.attains | 271 |
| abstract_inverted_index.between | 229 |
| abstract_inverted_index.company | 27 |
| abstract_inverted_index.corpora | 166 |
| abstract_inverted_index.current | 24 |
| abstract_inverted_index.dataset | 142 |
| abstract_inverted_index.decoder | 210 |
| abstract_inverted_index.despite | 279 |
| abstract_inverted_index.domain. | 189 |
| abstract_inverted_index.employs | 152 |
| abstract_inverted_index.encoder | 208 |
| abstract_inverted_index.extract | 109 |
| abstract_inverted_index.holder. | 77 |
| abstract_inverted_index.insight | 123 |
| abstract_inverted_index.labeled | 141, 155 |
| abstract_inverted_index.methods | 247 |
| abstract_inverted_index.million | 203 |
| abstract_inverted_index.overall | 36 |
| abstract_inverted_index.present | 43 |
| abstract_inverted_index.queries | 98 |
| abstract_inverted_index.related | 99 |
| abstract_inverted_index.reports | 9, 28, 278 |
| abstract_inverted_index.results | 238, 252 |
| abstract_inverted_index.several | 202, 213 |
| abstract_inverted_index.streams | 55 |
| abstract_inverted_index.surpass | 244 |
| abstract_inverted_index.textual | 90 |
| abstract_inverted_index.variety | 162 |
| abstract_inverted_index.FinBERT, | 174 |
| abstract_inverted_index.accuracy | 273 |
| abstract_inverted_index.analysis | 146 |
| abstract_inverted_index.compared | 255 |
| abstract_inverted_index.dataset. | 264 |
| abstract_inverted_index.daunting | 82 |
| abstract_inverted_index.decision | 74 |
| abstract_inverted_index.existing | 258 |
| abstract_inverted_index.interest | 104 |
| abstract_inverted_index.language | 150 |
| abstract_inverted_index.markets. | 132 |
| abstract_inverted_index.observed | 267 |
| abstract_inverted_index.proposed | 191 |
| abstract_inverted_index.provides | 124 |
| abstract_inverted_index.relation | 228 |
| abstract_inverted_index.specific | 165 |
| abstract_inverted_index.training | 283 |
| abstract_inverted_index.Financial | 53 |
| abstract_inverted_index.Investors | 1 |
| abstract_inverted_index.Quarterly | 26 |
| abstract_inverted_index.analysis. | 170 |
| abstract_inverted_index.attention | 199, 214 |
| abstract_inverted_index.company's | 23, 35 |
| abstract_inverted_index.comprises | 211 |
| abstract_inverted_index.datasets. | 250 |
| abstract_inverted_index.explosion | 102 |
| abstract_inverted_index.financial | 3, 73, 131, 144, 168, 188, 249, 263 |
| abstract_inverted_index.important | 70, 97 |
| abstract_inverted_index.including | 167 |
| abstract_inverted_index.instances | 224 |
| abstract_inverted_index.necessary | 84 |
| abstract_inverted_index.presented | 242 |
| abstract_inverted_index.procedure | 86 |
| abstract_inverted_index.quarterly | 8, 57, 277 |
| abstract_inverted_index.recognize | 223 |
| abstract_inverted_index.regarding | 127 |
| abstract_inverted_index.represent | 64 |
| abstract_inverted_index.sentence. | 235 |
| abstract_inverted_index.sentiment | 145, 169 |
| abstract_inverted_index.situation | 44 |
| abstract_inverted_index.strongest | 227 |
| abstract_inverted_index.abstracted | 31 |
| abstract_inverted_index.challenges | 185 |
| abstract_inverted_index.framework, | 181 |
| abstract_inverted_index.naturally, | 61 |
| abstract_inverted_index.parameters | 156 |
| abstract_inverted_index.particular | 234 |
| abstract_inverted_index.processing | 225 |
| abstract_inverted_index.arrangement | 220 |
| abstract_inverted_index.cooperative | 65 |
| abstract_inverted_index.expressions | 66 |
| abstract_inverted_index.information | 111 |
| abstract_inverted_index.linguistics | 184 |
| abstract_inverted_index.parameters. | 204 |
| abstract_inverted_index.perspective | 32 |
| abstract_inverted_index.pre-trained | 149 |
| abstract_inverted_index.transformer | 195 |
| abstract_inverted_index.availability | 138 |
| abstract_inverted_index.intelligence | 87 |
| abstract_inverted_index.performance, | 38 |
| abstract_inverted_index.performance. | 25 |
| abstract_inverted_index.unstructured | 60, 89, 113 |
| abstract_inverted_index.applications. | 147 |
| abstract_inverted_index.professionals | 4 |
| abstract_inverted_index.experimentation | 237 |
| abstract_inverted_index.state-of-the-art | 246 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.86151523 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |