Sentiment analysis model for cryptocurrency tweets using different deep learning techniques Article Swipe
M. Thamban Nair
,
Laila A. Abd-Elmegid
,
Mohamed I. Marie
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1515/jisys-2023-0085
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1515/jisys-2023-0085
Bitcoin (BTC) is one of the most important cryptocurrencies widely used in various financial and commercial transactions due to the fluctuations in the price of this currency. Recent research in large data analytics and natural language processing has resulted in the development of automated techniques for assessing the sentiment in online communities, which has emerged as a crucial platform for users to express their thoughts and comments. Twitter, one of the most well-known social media platforms, provides many tweets about the BTC cryptocurrency. With this knowledge, we can apply deep learning (DL) to use these data to predict BTC price variations. The researchers are interested in studying and analyzing the reasons contributing to the BTC price’s erratic movement by analyzing Twitter sentiment. The main problem in this article is that no standard model with high accuracy can be relied upon in analyzing textual emotions, as it represents one of the factors affecting the rise and fall in the price of cryptocurrencies. This article aims to classify the sentiments of an expression into positive, negative, or neutral emotions. The methods that have been used are word embedding FastText model in addition to different DL methods that deal with time series, one-dimensional convolutional neural networks (CONV1D), long-short-term memory networks (LSTMs), recurrent neural networks, gated recurrent units, and a Bi-LSTM + CONV1D The main results revealed that the LSTM method, based on the DL technique, achieved the best results. The performance accuracy of the methods was 95.01, 95.95, 80.59, 95.82, and 95.67%, respectively. Thus, we conclude that the LSTM method achieved better results than other methods in analyzing the textual sentiment of BTC.
Related Topics
Concepts
Cryptocurrency
Computer science
Sentiment analysis
Word embedding
Artificial intelligence
Currency
Deep learning
Convolutional neural network
Social media
Big data
Machine learning
Word (group theory)
Recurrent neural network
Analytics
Artificial neural network
Natural language processing
Embedding
Data mining
World Wide Web
Philosophy
Linguistics
Monetary economics
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1515/jisys-2023-0085
- https://www.degruyter.com/document/doi/10.1515/jisys-2023-0085/pdf
- OA Status
- gold
- Cited By
- 7
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392644159
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4392644159Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1515/jisys-2023-0085Digital Object Identifier
- Title
-
Sentiment analysis model for cryptocurrency tweets using different deep learning techniquesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
M. Thamban Nair, Laila A. Abd-Elmegid, Mohamed I. MarieList of authors in order
- Landing page
-
https://doi.org/10.1515/jisys-2023-0085Publisher landing page
- PDF URL
-
https://www.degruyter.com/document/doi/10.1515/jisys-2023-0085/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.degruyter.com/document/doi/10.1515/jisys-2023-0085/pdfDirect OA link when available
- Concepts
-
Cryptocurrency, Computer science, Sentiment analysis, Word embedding, Artificial intelligence, Currency, Deep learning, Convolutional neural network, Social media, Big data, Machine learning, Word (group theory), Recurrent neural network, Analytics, Artificial neural network, Natural language processing, Embedding, Data mining, World Wide Web, Philosophy, Linguistics, Monetary economics, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 3Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4392644159 |
|---|---|
| doi | https://doi.org/10.1515/jisys-2023-0085 |
| ids.doi | https://doi.org/10.1515/jisys-2023-0085 |
| ids.openalex | https://openalex.org/W4392644159 |
| fwci | 10.69355184 |
| type | article |
| title | Sentiment analysis model for cryptocurrency tweets using different deep learning techniques |
| biblio.issue | 1 |
| biblio.volume | 33 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10270 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1710 |
| topics[0].subfield.display_name | Information Systems |
| topics[0].display_name | Blockchain Technology Applications and Security |
| topics[1].id | https://openalex.org/T11326 |
| topics[1].field.id | https://openalex.org/fields/18 |
| topics[1].field.display_name | Decision Sciences |
| topics[1].score | 0.9878000020980835 |
| 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 | Stock Market Forecasting Methods |
| topics[2].id | https://openalex.org/T10664 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9807999730110168 |
| 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 | Sentiment Analysis and Opinion Mining |
| is_xpac | False |
| apc_list.value | 1000 |
| apc_list.currency | EUR |
| apc_list.value_usd | 1078 |
| apc_paid.value | 1000 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 1078 |
| concepts[0].id | https://openalex.org/C180706569 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8473283052444458 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q13479982 |
| concepts[0].display_name | Cryptocurrency |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.8161104321479797 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C66402592 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7578031420707703 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2271421 |
| concepts[2].display_name | Sentiment analysis |
| concepts[3].id | https://openalex.org/C2777462759 |
| concepts[3].level | 3 |
| concepts[3].score | 0.6713767051696777 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q18395344 |
| concepts[3].display_name | Word embedding |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.6292269825935364 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C141121606 |
| concepts[5].level | 2 |
| concepts[5].score | 0.6073793768882751 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q8142 |
| concepts[5].display_name | Currency |
| concepts[6].id | https://openalex.org/C108583219 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5805248618125916 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[6].display_name | Deep learning |
| concepts[7].id | https://openalex.org/C81363708 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5600115656852722 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[7].display_name | Convolutional neural network |
| concepts[8].id | https://openalex.org/C518677369 |
| concepts[8].level | 2 |
| concepts[8].score | 0.5119708776473999 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q202833 |
| concepts[8].display_name | Social media |
| concepts[9].id | https://openalex.org/C75684735 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4784267544746399 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q858810 |
| concepts[9].display_name | Big data |
| concepts[10].id | https://openalex.org/C119857082 |
| concepts[10].level | 1 |
| concepts[10].score | 0.4688728153705597 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[10].display_name | Machine learning |
| concepts[11].id | https://openalex.org/C90805587 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4664084017276764 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q10944557 |
| concepts[11].display_name | Word (group theory) |
| concepts[12].id | https://openalex.org/C147168706 |
| concepts[12].level | 3 |
| concepts[12].score | 0.45880380272865295 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1457734 |
| concepts[12].display_name | Recurrent neural network |
| concepts[13].id | https://openalex.org/C79158427 |
| concepts[13].level | 2 |
| concepts[13].score | 0.42910975217819214 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q485396 |
| concepts[13].display_name | Analytics |
| concepts[14].id | https://openalex.org/C50644808 |
| concepts[14].level | 2 |
| concepts[14].score | 0.39315611124038696 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[14].display_name | Artificial neural network |
| concepts[15].id | https://openalex.org/C204321447 |
| concepts[15].level | 1 |
| concepts[15].score | 0.37920260429382324 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[15].display_name | Natural language processing |
| concepts[16].id | https://openalex.org/C41608201 |
| concepts[16].level | 2 |
| concepts[16].score | 0.3655473589897156 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q980509 |
| concepts[16].display_name | Embedding |
| concepts[17].id | https://openalex.org/C124101348 |
| concepts[17].level | 1 |
| concepts[17].score | 0.2768042981624603 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[17].display_name | Data mining |
| concepts[18].id | https://openalex.org/C136764020 |
| concepts[18].level | 1 |
| concepts[18].score | 0.17702960968017578 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[18].display_name | World Wide Web |
| concepts[19].id | https://openalex.org/C138885662 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[19].display_name | Philosophy |
| concepts[20].id | https://openalex.org/C41895202 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[20].display_name | Linguistics |
| concepts[21].id | https://openalex.org/C556758197 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q580018 |
| concepts[21].display_name | Monetary economics |
| concepts[22].id | https://openalex.org/C162324750 |
| concepts[22].level | 0 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[22].display_name | Economics |
| keywords[0].id | https://openalex.org/keywords/cryptocurrency |
| keywords[0].score | 0.8473283052444458 |
| keywords[0].display_name | Cryptocurrency |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.8161104321479797 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/sentiment-analysis |
| keywords[2].score | 0.7578031420707703 |
| keywords[2].display_name | Sentiment analysis |
| keywords[3].id | https://openalex.org/keywords/word-embedding |
| keywords[3].score | 0.6713767051696777 |
| keywords[3].display_name | Word embedding |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.6292269825935364 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/currency |
| keywords[5].score | 0.6073793768882751 |
| keywords[5].display_name | Currency |
| keywords[6].id | https://openalex.org/keywords/deep-learning |
| keywords[6].score | 0.5805248618125916 |
| keywords[6].display_name | Deep learning |
| keywords[7].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[7].score | 0.5600115656852722 |
| keywords[7].display_name | Convolutional neural network |
| keywords[8].id | https://openalex.org/keywords/social-media |
| keywords[8].score | 0.5119708776473999 |
| keywords[8].display_name | Social media |
| keywords[9].id | https://openalex.org/keywords/big-data |
| keywords[9].score | 0.4784267544746399 |
| keywords[9].display_name | Big data |
| keywords[10].id | https://openalex.org/keywords/machine-learning |
| keywords[10].score | 0.4688728153705597 |
| keywords[10].display_name | Machine learning |
| keywords[11].id | https://openalex.org/keywords/word |
| keywords[11].score | 0.4664084017276764 |
| keywords[11].display_name | Word (group theory) |
| keywords[12].id | https://openalex.org/keywords/recurrent-neural-network |
| keywords[12].score | 0.45880380272865295 |
| keywords[12].display_name | Recurrent neural network |
| keywords[13].id | https://openalex.org/keywords/analytics |
| keywords[13].score | 0.42910975217819214 |
| keywords[13].display_name | Analytics |
| keywords[14].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[14].score | 0.39315611124038696 |
| keywords[14].display_name | Artificial neural network |
| keywords[15].id | https://openalex.org/keywords/natural-language-processing |
| keywords[15].score | 0.37920260429382324 |
| keywords[15].display_name | Natural language processing |
| keywords[16].id | https://openalex.org/keywords/embedding |
| keywords[16].score | 0.3655473589897156 |
| keywords[16].display_name | Embedding |
| keywords[17].id | https://openalex.org/keywords/data-mining |
| keywords[17].score | 0.2768042981624603 |
| keywords[17].display_name | Data mining |
| keywords[18].id | https://openalex.org/keywords/world-wide-web |
| keywords[18].score | 0.17702960968017578 |
| keywords[18].display_name | World Wide Web |
| language | en |
| locations[0].id | doi:10.1515/jisys-2023-0085 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764846071 |
| locations[0].source.issn | 0334-1860, 2191-026X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 0334-1860 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Journal of Intelligent Systems |
| locations[0].source.host_organization | https://openalex.org/P4310315148 |
| locations[0].source.host_organization_name | IlmuKomputer.Com |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310315148 |
| locations[0].source.host_organization_lineage_names | IlmuKomputer.Com |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.degruyter.com/document/doi/10.1515/jisys-2023-0085/pdf |
| 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 | Journal of Intelligent Systems |
| locations[0].landing_page_url | https://doi.org/10.1515/jisys-2023-0085 |
| locations[1].id | pmh:oai:doaj.org/article:77c1760d603440bf9653b591746d93a4 |
| 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 | Journal of Intelligent Systems, Vol 33, Iss 1, Pp 18803-27 (2024) |
| locations[1].landing_page_url | https://doaj.org/article/77c1760d603440bf9653b591746d93a4 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5022301901 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6982-1921 |
| authorships[0].author.display_name | M. Thamban Nair |
| authorships[0].countries | EG |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210152378 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Management Information Systems, Higher Institute for Qualitative Studies, Heliopolis , Cairo 11771 , Egypt |
| authorships[0].institutions[0].id | https://openalex.org/I4210152378 |
| authorships[0].institutions[0].ror | https://ror.org/049jpg876 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210152378 |
| authorships[0].institutions[0].country_code | EG |
| authorships[0].institutions[0].display_name | Higher Institute for Qualitative Studies |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Michael Nair |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Department of Management Information Systems, Higher Institute for Qualitative Studies, Heliopolis , Cairo 11771 , Egypt |
| authorships[1].author.id | https://openalex.org/A5057607344 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-8064-8530 |
| authorships[1].author.display_name | Laila A. Abd-Elmegid |
| authorships[1].countries | EG |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I84058292 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University , Cairo 11795 , Egypt |
| authorships[1].institutions[0].id | https://openalex.org/I84058292 |
| authorships[1].institutions[0].ror | https://ror.org/00h55v928 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I84058292 |
| authorships[1].institutions[0].country_code | EG |
| authorships[1].institutions[0].display_name | Helwan University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Laila A. Abd-Elmegid |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University , Cairo 11795 , Egypt |
| authorships[2].author.id | https://openalex.org/A5058811396 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-4784-2953 |
| authorships[2].author.display_name | Mohamed I. Marie |
| authorships[2].countries | EG |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I84058292 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University , Cairo 11795 , Egypt |
| authorships[2].institutions[0].id | https://openalex.org/I84058292 |
| authorships[2].institutions[0].ror | https://ror.org/00h55v928 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I84058292 |
| authorships[2].institutions[0].country_code | EG |
| authorships[2].institutions[0].display_name | Helwan University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Mohamed I. Marie |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University , Cairo 11795 , Egypt |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.degruyter.com/document/doi/10.1515/jisys-2023-0085/pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Sentiment analysis model for cryptocurrency tweets using different deep learning techniques |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-12-06T23:10:59.065948 |
| primary_topic.id | https://openalex.org/T10270 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1710 |
| primary_topic.subfield.display_name | Information Systems |
| primary_topic.display_name | Blockchain Technology Applications and Security |
| related_works | https://openalex.org/W4366411693, https://openalex.org/W3211641817, https://openalex.org/W3164717803, https://openalex.org/W4389915954, https://openalex.org/W3123387860, https://openalex.org/W4291291739, https://openalex.org/W3186997021, https://openalex.org/W4200618314, https://openalex.org/W4308088897, https://openalex.org/W2944541365 |
| cited_by_count | 7 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 4 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 3 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1515/jisys-2023-0085 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764846071 |
| best_oa_location.source.issn | 0334-1860, 2191-026X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 0334-1860 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Journal of Intelligent Systems |
| best_oa_location.source.host_organization | https://openalex.org/P4310315148 |
| best_oa_location.source.host_organization_name | IlmuKomputer.Com |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310315148 |
| best_oa_location.source.host_organization_lineage_names | IlmuKomputer.Com |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.degruyter.com/document/doi/10.1515/jisys-2023-0085/pdf |
| 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 | Journal of Intelligent Systems |
| best_oa_location.landing_page_url | https://doi.org/10.1515/jisys-2023-0085 |
| primary_location.id | doi:10.1515/jisys-2023-0085 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764846071 |
| primary_location.source.issn | 0334-1860, 2191-026X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 0334-1860 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Journal of Intelligent Systems |
| primary_location.source.host_organization | https://openalex.org/P4310315148 |
| primary_location.source.host_organization_name | IlmuKomputer.Com |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310315148 |
| primary_location.source.host_organization_lineage_names | IlmuKomputer.Com |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.degruyter.com/document/doi/10.1515/jisys-2023-0085/pdf |
| 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 | Journal of Intelligent Systems |
| primary_location.landing_page_url | https://doi.org/10.1515/jisys-2023-0085 |
| publication_date | 2024-01-01 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4386051784, https://openalex.org/W4226185762, https://openalex.org/W3206339540, https://openalex.org/W3131679054, https://openalex.org/W4283216460, https://openalex.org/W3011605097, https://openalex.org/W4224213870, https://openalex.org/W4224283955, https://openalex.org/W4297094543, https://openalex.org/W3037583678, https://openalex.org/W3038105607, https://openalex.org/W3093859549, https://openalex.org/W4281401850, https://openalex.org/W4239522099, https://openalex.org/W4283784468, https://openalex.org/W4226026750, https://openalex.org/W3158053678, https://openalex.org/W3033470039, https://openalex.org/W2906216723, https://openalex.org/W4381952928, https://openalex.org/W4380201950, https://openalex.org/W4380368768, https://openalex.org/W4220726338, https://openalex.org/W3022935508, https://openalex.org/W3118913631, https://openalex.org/W2469351952, https://openalex.org/W3000739907, https://openalex.org/W2617241599, https://openalex.org/W3046402235, https://openalex.org/W3195446119, https://openalex.org/W4292858715, https://openalex.org/W4225697732, https://openalex.org/W4224014209, https://openalex.org/W4206081398, https://openalex.org/W4200266487, https://openalex.org/W4281706879, https://openalex.org/W4235645554, https://openalex.org/W4252714967, https://openalex.org/W4307142726, https://openalex.org/W3118321697, https://openalex.org/W4210379867, https://openalex.org/W3130109406, https://openalex.org/W2941799245 |
| referenced_works_count | 43 |
| abstract_inverted_index.+ | 218 |
| abstract_inverted_index.a | 57, 216 |
| abstract_inverted_index.DL | 193, 231 |
| abstract_inverted_index.an | 170 |
| abstract_inverted_index.as | 56, 145 |
| abstract_inverted_index.be | 138 |
| abstract_inverted_index.by | 119 |
| abstract_inverted_index.in | 12, 22, 30, 40, 50, 106, 126, 141, 157, 189, 264 |
| abstract_inverted_index.is | 3, 129 |
| abstract_inverted_index.it | 146 |
| abstract_inverted_index.no | 131 |
| abstract_inverted_index.of | 5, 25, 43, 70, 149, 160, 169, 240, 269 |
| abstract_inverted_index.on | 229 |
| abstract_inverted_index.or | 175 |
| abstract_inverted_index.to | 19, 62, 93, 97, 113, 165, 191 |
| abstract_inverted_index.we | 87, 252 |
| abstract_inverted_index.BTC | 82, 99, 115 |
| abstract_inverted_index.The | 102, 123, 178, 220, 237 |
| abstract_inverted_index.and | 15, 34, 66, 108, 155, 215, 248 |
| abstract_inverted_index.are | 104, 184 |
| abstract_inverted_index.can | 88, 137 |
| abstract_inverted_index.due | 18 |
| abstract_inverted_index.for | 46, 60 |
| abstract_inverted_index.has | 38, 54 |
| abstract_inverted_index.one | 4, 69, 148 |
| abstract_inverted_index.the | 6, 20, 23, 41, 48, 71, 81, 110, 114, 150, 153, 158, 167, 225, 230, 234, 241, 255, 266 |
| abstract_inverted_index.use | 94 |
| abstract_inverted_index.was | 243 |
| abstract_inverted_index.(DL) | 92 |
| abstract_inverted_index.BTC. | 270 |
| abstract_inverted_index.LSTM | 226, 256 |
| abstract_inverted_index.This | 162 |
| abstract_inverted_index.With | 84 |
| abstract_inverted_index.aims | 164 |
| abstract_inverted_index.been | 182 |
| abstract_inverted_index.best | 235 |
| abstract_inverted_index.data | 32, 96 |
| abstract_inverted_index.deal | 196 |
| abstract_inverted_index.deep | 90 |
| abstract_inverted_index.fall | 156 |
| abstract_inverted_index.have | 181 |
| abstract_inverted_index.high | 135 |
| abstract_inverted_index.into | 172 |
| abstract_inverted_index.main | 124, 221 |
| abstract_inverted_index.many | 78 |
| abstract_inverted_index.most | 7, 72 |
| abstract_inverted_index.rise | 154 |
| abstract_inverted_index.than | 261 |
| abstract_inverted_index.that | 130, 180, 195, 224, 254 |
| abstract_inverted_index.this | 26, 85, 127 |
| abstract_inverted_index.time | 198 |
| abstract_inverted_index.upon | 140 |
| abstract_inverted_index.used | 11, 183 |
| abstract_inverted_index.with | 134, 197 |
| abstract_inverted_index.word | 185 |
| abstract_inverted_index.(BTC) | 2 |
| abstract_inverted_index.Thus, | 251 |
| abstract_inverted_index.about | 80 |
| abstract_inverted_index.apply | 89 |
| abstract_inverted_index.based | 228 |
| abstract_inverted_index.gated | 212 |
| abstract_inverted_index.large | 31 |
| abstract_inverted_index.media | 75 |
| abstract_inverted_index.model | 133, 188 |
| abstract_inverted_index.other | 262 |
| abstract_inverted_index.price | 24, 100, 159 |
| abstract_inverted_index.their | 64 |
| abstract_inverted_index.these | 95 |
| abstract_inverted_index.users | 61 |
| abstract_inverted_index.which | 53 |
| abstract_inverted_index.80.59, | 246 |
| abstract_inverted_index.95.01, | 244 |
| abstract_inverted_index.95.82, | 247 |
| abstract_inverted_index.95.95, | 245 |
| abstract_inverted_index.CONV1D | 219 |
| abstract_inverted_index.Recent | 28 |
| abstract_inverted_index.better | 259 |
| abstract_inverted_index.memory | 206 |
| abstract_inverted_index.method | 257 |
| abstract_inverted_index.neural | 202, 210 |
| abstract_inverted_index.online | 51 |
| abstract_inverted_index.relied | 139 |
| abstract_inverted_index.social | 74 |
| abstract_inverted_index.tweets | 79 |
| abstract_inverted_index.units, | 214 |
| abstract_inverted_index.widely | 10 |
| abstract_inverted_index.95.67%, | 249 |
| abstract_inverted_index.Bi-LSTM | 217 |
| abstract_inverted_index.Bitcoin | 1 |
| abstract_inverted_index.Twitter | 121 |
| abstract_inverted_index.article | 128, 163 |
| abstract_inverted_index.crucial | 58 |
| abstract_inverted_index.emerged | 55 |
| abstract_inverted_index.erratic | 117 |
| abstract_inverted_index.express | 63 |
| abstract_inverted_index.factors | 151 |
| abstract_inverted_index.method, | 227 |
| abstract_inverted_index.methods | 179, 194, 242, 263 |
| abstract_inverted_index.natural | 35 |
| abstract_inverted_index.neutral | 176 |
| abstract_inverted_index.predict | 98 |
| abstract_inverted_index.problem | 125 |
| abstract_inverted_index.reasons | 111 |
| abstract_inverted_index.results | 222, 260 |
| abstract_inverted_index.series, | 199 |
| abstract_inverted_index.textual | 143, 267 |
| abstract_inverted_index.various | 13 |
| abstract_inverted_index.(LSTMs), | 208 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.FastText | 187 |
| abstract_inverted_index.Twitter, | 68 |
| abstract_inverted_index.accuracy | 136, 239 |
| abstract_inverted_index.achieved | 233, 258 |
| abstract_inverted_index.addition | 190 |
| abstract_inverted_index.classify | 166 |
| abstract_inverted_index.conclude | 253 |
| abstract_inverted_index.language | 36 |
| abstract_inverted_index.learning | 91 |
| abstract_inverted_index.movement | 118 |
| abstract_inverted_index.networks | 203, 207 |
| abstract_inverted_index.platform | 59 |
| abstract_inverted_index.provides | 77 |
| abstract_inverted_index.research | 29 |
| abstract_inverted_index.resulted | 39 |
| abstract_inverted_index.results. | 236 |
| abstract_inverted_index.revealed | 223 |
| abstract_inverted_index.standard | 132 |
| abstract_inverted_index.studying | 107 |
| abstract_inverted_index.thoughts | 65 |
| abstract_inverted_index.(CONV1D), | 204 |
| abstract_inverted_index.affecting | 152 |
| abstract_inverted_index.analytics | 33 |
| abstract_inverted_index.analyzing | 109, 120, 142, 265 |
| abstract_inverted_index.assessing | 47 |
| abstract_inverted_index.automated | 44 |
| abstract_inverted_index.comments. | 67 |
| abstract_inverted_index.currency. | 27 |
| abstract_inverted_index.different | 192 |
| abstract_inverted_index.embedding | 186 |
| abstract_inverted_index.emotions, | 144 |
| abstract_inverted_index.emotions. | 177 |
| abstract_inverted_index.financial | 14 |
| abstract_inverted_index.important | 8 |
| abstract_inverted_index.negative, | 174 |
| abstract_inverted_index.networks, | 211 |
| abstract_inverted_index.positive, | 173 |
| abstract_inverted_index.price’s | 116 |
| abstract_inverted_index.recurrent | 209, 213 |
| abstract_inverted_index.sentiment | 49, 268 |
| abstract_inverted_index.commercial | 16 |
| abstract_inverted_index.expression | 171 |
| abstract_inverted_index.interested | 105 |
| abstract_inverted_index.knowledge, | 86 |
| abstract_inverted_index.platforms, | 76 |
| abstract_inverted_index.processing | 37 |
| abstract_inverted_index.represents | 147 |
| abstract_inverted_index.sentiment. | 122 |
| abstract_inverted_index.sentiments | 168 |
| abstract_inverted_index.technique, | 232 |
| abstract_inverted_index.techniques | 45 |
| abstract_inverted_index.well-known | 73 |
| abstract_inverted_index.development | 42 |
| abstract_inverted_index.performance | 238 |
| abstract_inverted_index.researchers | 103 |
| abstract_inverted_index.variations. | 101 |
| abstract_inverted_index.communities, | 52 |
| abstract_inverted_index.contributing | 112 |
| abstract_inverted_index.fluctuations | 21 |
| abstract_inverted_index.transactions | 17 |
| abstract_inverted_index.convolutional | 201 |
| abstract_inverted_index.respectively. | 250 |
| abstract_inverted_index.cryptocurrency. | 83 |
| abstract_inverted_index.long-short-term | 205 |
| abstract_inverted_index.one-dimensional | 200 |
| abstract_inverted_index.cryptocurrencies | 9 |
| abstract_inverted_index.cryptocurrencies. | 161 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5022301901 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I4210152378 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.96754275 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |