team UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approach
Sentiment analysis in the financial domain is a challenging task that plays a crucial role in understanding public opinion, monitoring market trends, and assessing the impact of news on economic agents. In this shared task, we address targeted sentiment analysis in the financial domain, focusing on...
- Autores:
-
Cuadrado, Juan
Martinez, Elizabeth
Martinez-Santos, Juan Carlos
Puertas, Edwin
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12584
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12584
https://ceur-ws.org/Vol-3496/finances-paper4.pdf
- Palabra clave:
- Embeddings
FinancES
Phonestheme
Sentiment Analysis
Transformers
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| id |
UTB2_9f0899d0894f54f8d3ea5485795173ea |
|---|---|
| oai_identifier_str |
oai:repositorio.utb.edu.co:20.500.12585/12584 |
| network_acronym_str |
UTB2 |
| network_name_str |
Repositorio Institucional UTB |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
team UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approach |
| title |
team UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approach |
| spellingShingle |
team UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approach Embeddings FinancES Phonestheme Sentiment Analysis Transformers LEMB |
| title_short |
team UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approach |
| title_full |
team UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approach |
| title_fullStr |
team UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approach |
| title_full_unstemmed |
team UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approach |
| title_sort |
team UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approach |
| dc.creator.fl_str_mv |
Cuadrado, Juan Martinez, Elizabeth Martinez-Santos, Juan Carlos Puertas, Edwin |
| dc.contributor.author.none.fl_str_mv |
Cuadrado, Juan Martinez, Elizabeth Martinez-Santos, Juan Carlos Puertas, Edwin |
| dc.subject.keywords.spa.fl_str_mv |
Embeddings FinancES Phonestheme Sentiment Analysis Transformers |
| topic |
Embeddings FinancES Phonestheme Sentiment Analysis Transformers LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
Sentiment analysis in the financial domain is a challenging task that plays a crucial role in understanding public opinion, monitoring market trends, and assessing the impact of news on economic agents. In this shared task, we address targeted sentiment analysis in the financial domain, focusing on identifying the main economic target in news headlines and determining the sentiment polarity towards such targets. We propose a methodology that combines transformer-based models and phonestheme embeddings to extract meaningful features from the text, which are then used in a support vector machine (SVM) classifier for sentiment classification. Our approach shows promising results, outperforming the baseline with an F1-score of 0.529229 in Task 1. This research contributes to financial sentiment analysis by addressing the complexity of financial language and considering multiple economic agents' perspectives. |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-12-06T19:38:33Z |
| dc.date.available.none.fl_str_mv |
2023-12-06T19:38:33Z |
| dc.date.issued.none.fl_str_mv |
2023-12-06 |
| dc.date.submitted.none.fl_str_mv |
2023-12-06 |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/draft |
| dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
draft |
| dc.identifier.citation.spa.fl_str_mv |
Cuadrado, J., Martinez, E., Martinez-Santos, J. C., & Puertas, E. (2023). Team UTB-NLP at FinancES 2023: Financial Targeted Sentiment Analysis Using a Phonestheme Semantic Approach. In Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2023), co-located with the 39th Conference of the Spanish Society for Natural Language Processing (SEPLN 2023), CEUR-WS. org. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/12584 |
| dc.identifier.url.none.fl_str_mv |
https://ceur-ws.org/Vol-3496/finances-paper4.pdf |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Cuadrado, J., Martinez, E., Martinez-Santos, J. C., & Puertas, E. (2023). Team UTB-NLP at FinancES 2023: Financial Targeted Sentiment Analysis Using a Phonestheme Semantic Approach. In Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2023), co-located with the 39th Conference of the Spanish Society for Natural Language Processing (SEPLN 2023), CEUR-WS. org. Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/12584 https://ceur-ws.org/Vol-3496/finances-paper4.pdf |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.uri.*.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.cc.*.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.none.fl_str_mv |
12 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
| dc.publisher.sede.spa.fl_str_mv |
Campus Tecnológico |
| dc.publisher.discipline.spa.fl_str_mv |
Maestría en Ingeniería |
| dc.source.spa.fl_str_mv |
Iberian Languages Evaluation Forum |
| institution |
Universidad Tecnológica de Bolívar |
| bitstream.url.fl_str_mv |
https://repositorio.utb.edu.co/bitstream/20.500.12585/12584/1/finances-paper4.pdf https://repositorio.utb.edu.co/bitstream/20.500.12585/12584/2/license_rdf https://repositorio.utb.edu.co/bitstream/20.500.12585/12584/3/license.txt https://repositorio.utb.edu.co/bitstream/20.500.12585/12584/4/finances-paper4.pdf.txt https://repositorio.utb.edu.co/bitstream/20.500.12585/12584/5/finances-paper4.pdf.jpg |
| bitstream.checksum.fl_str_mv |
e096d6ac68529dcee3517d71a8c8981b 4460e5956bc1d1639be9ae6146a50347 e20ad307a1c5f3f25af9304a7a7c86b6 6831f44596e5f7d396bda281c1250394 fdff0c96246960fa19ce10c7d2ceca1e |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional UTB |
| repository.mail.fl_str_mv |
repositorioutb@utb.edu.co |
| _version_ |
1814021660854976512 |
| spelling |
Cuadrado, Juan73b693c6-9993-4025-9268-0f1bbe13b105Martinez, Elizabeth4ebda059-55c6-4e72-8ce5-81181da731b4Martinez-Santos, Juan Carlos5c958644-c78d-401d-8ba9-bbd39fe77318Puertas, Edwin5a1b1566-e112-43dc-8ac7-310ea9af8f052023-12-06T19:38:33Z2023-12-06T19:38:33Z2023-12-062023-12-06Cuadrado, J., Martinez, E., Martinez-Santos, J. C., & Puertas, E. (2023). Team UTB-NLP at FinancES 2023: Financial Targeted Sentiment Analysis Using a Phonestheme Semantic Approach. In Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2023), co-located with the 39th Conference of the Spanish Society for Natural Language Processing (SEPLN 2023), CEUR-WS. org.https://hdl.handle.net/20.500.12585/12584https://ceur-ws.org/Vol-3496/finances-paper4.pdfUniversidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarSentiment analysis in the financial domain is a challenging task that plays a crucial role in understanding public opinion, monitoring market trends, and assessing the impact of news on economic agents. In this shared task, we address targeted sentiment analysis in the financial domain, focusing on identifying the main economic target in news headlines and determining the sentiment polarity towards such targets. We propose a methodology that combines transformer-based models and phonestheme embeddings to extract meaningful features from the text, which are then used in a support vector machine (SVM) classifier for sentiment classification. Our approach shows promising results, outperforming the baseline with an F1-score of 0.529229 in Task 1. This research contributes to financial sentiment analysis by addressing the complexity of financial language and considering multiple economic agents' perspectives.Universidad Tecnológica de Bolívar12 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Iberian Languages Evaluation Forumteam UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approachinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1EmbeddingsFinancESPhonesthemeSentiment AnalysisTransformersLEMBCartagena de IndiasCampus TecnológicoMaestría en IngenieríaPúblico generalBowles, S. (2003). Microeconomics: behavior, institutions, and evolution. Princeton University Press.Hasan, M. M., Popp, J., & Oláh, J. (2020). Current landscape and influence of big data on finance. Journal of Big Data, 7(1), 1-17.Puertas, E., Martinez-Santos, J. C., & Pertuz-Duran, P. A. (2022, November). Presidential preferences in Colombia through Sentiment Analysis. In 2022 IEEE ANDESCON (pp. 1-5). IEEE.George, A. S., George, A. H., Baskar, T., & Martin, A. G. (2023). Human Insight AI: An Innovative Technology Bridging The Gap Between Humans And Machines For a Safe, Sustainable Future. Partners Universal International Research Journal, 2(1), 1-15.Man, X., Luo, T., & Lin, J. (2019, May). Financial sentiment analysis (fsa): A survey. In 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS) (pp. 617-622). IEEE.Zhao, L., Li, L., Zheng, X., & Zhang, J. (2021, May). A BERT based sentiment analysis and key entity detection approach for online financial texts. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 1233-1238). IEEE.Dashtipour, K., Gogate, M., Li, J., Jiang, F., Kong, B., & Hussain, A. (2020). A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks. Neurocomputing, 380, 1-10.Guarasci, R., Silvestri, S., De Pietro, G., Fujita, H., & Esposito, M. (2023). Assessing BERT’s ability to learn Italian syntax: A study on null-subject and agreement phenomena. Journal of Ambient Intelligence and Humanized Computing, 14(1), 289-303.García-Díaz, J. A., García-Sánchez, F., & Valencia-García, R. (2023). Smart analysis of economics sentiment in Spanish based on linguistic features and transformers. IEEE Access, 11, 14211-14224.Ishizuka, K., & Nakata, K. (2021, April). Text Mining for Factor Modeling of Japanese Stock Performance. In 2021 IEEE 8th International Conference on Industrial Engineering and Applications (ICIEA) (pp. 538-542). IEEE.Gutiérrez-Fandiño, A., Kolm, P. N., i Alonso, M. N., & Armengol-Estapé, J. (2022). FinEAS: Financial Embedding Analysis of Sentiment. The Journal of Financial Data Science, 4(3), 45-53.Jiménez-Zafra, S. M., Rangel, F., & Gómez, M. M. Y. (2023). Overview of IberLEF 2023: Natural Language Processing Challenges for Spanish and other Iberian Languages. In Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2023), co-located with the 39th Conference of the Spanish Society for Natural Language Processing (SEPLN 2023), CEURWS. org.Garcia-Díaz, J. A., Almela, Á., García-Sánchez, F., Alcaraz-Mármol, G., Marín, M. J., & Valencia-García, R. (2023). Overview of FinancES 2023: Financial Targeted Sentiment Analysis in Spanish. Procesamiento del Lenguaje Natural, 71, 417-423.García-Díaz, J. A., Salas-Zárate, M. P., Hernández-Alcaraz, M. L., Valencia-García, R., & Gómez-Berbís, J. M. (2018). Machine learning based sentiment analysis on Spanish financial tweets. In Trends and Advances in Information Systems and Technologies: Volume 1 6 (pp. 305-311). Springer International Publishing.Sun, F., Belatreche, A., Coleman, S., McGinnity, T. M., & Li, Y. (2014, March). Pre-processing online financial text for sentiment classification: A natural language processing approach. In 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (pp. 122-129). IEEE.Puertas, E., & Martinez-Santos, J. C. (2021). Phonetic detection for hate speech spreaders on Twitter.Husein, A. M., Sipahutar, B., Dashuah, R., & Hutauruk, E. (2023). Sentiment Analysis Od Face To Face School Policy On Twitter Social Media With Support Vector Machine (SVM). Sinkron: jurnal dan penelitian teknik informatika, 8(1), 480-486.Puertas, E. (2020). Embedding of phonestheme in Spanish (1.0). Zenodo. https://doi.org/10.5281/zenodo.4299242Moreno-Sandoval, L. G., Del Castillo, E. A. P., Quimbaya, A. P., & Alvarado-Valencia, J. A. (2020). Assembly of Polarity, Emotion and User Statistics for Detection of Fake Profiles. In CLEF (Working Notes).Hays, C., Schutzman, Z., Raghavan, M., Walk, E., & Zimmer, P. (2023, April). Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection. In Proceedings of the ACM Web Conference 2023 (pp. 3660-3669).Nemes, L., & Kiss, A. (2021). Prediction of stock values changes using sentiment analysis of stock news headlines. Journal of Information and Telecommunication, 5(3), 375-394.Zhang, H., Li, Z., Xie, H., Lau, R. Y., Cheng, G., Li, Q., & Zhang, D. (2022). Leveraging statistical information in fine-grained financial sentiment analysis. World Wide Web, 25(2), 513-531.Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.Valle-Cruz, D., Fernandez-Cortez, V., López-Chau, A., & Sandoval-Almazán, R. (2022). Does twitter affect stock market decisions? financial sentiment analysis during pandemics: A comparative study of the h1n1 and the covid-19 periods. Cognitive computation, 1-16.Puertas Del Castillo, E. A. Análisis de elementos fonéticos y elementos emocionales para predecir la polaridad en fuentes de microblogging.Puertas, E., Moreno-Sandoval, L. G., Redondo, J., Alvarado-Valencia, J. A., & Pomares-Quimbaya, A. (2021). Detection of sociolinguistic features in digital social networks for the detection of communities. Cognitive Computation, 13, 518-537.Pan, R., García-Díaz, J. A., Garcia-Sanchez, F., & Valencia-García, R. (2023). Evaluation of transformer models for financial targeted sentiment analysis in Spanish. PeerJ Computer Science, 9, e1377.Pérez, J. M., Furman, D. A., Alemany, L. A., & Luque, F. (2021). Robertuito: a pre-trained language model for social media text in spanish. arXiv preprint arXiv:2111.09453.http://purl.org/coar/resource_type/c_6501ORIGINALfinances-paper4.pdffinances-paper4.pdfapplication/pdf892658https://repositorio.utb.edu.co/bitstream/20.500.12585/12584/1/finances-paper4.pdfe096d6ac68529dcee3517d71a8c8981bMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/12584/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/12584/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXTfinances-paper4.pdf.txtfinances-paper4.pdf.txtExtracted texttext/plain32387https://repositorio.utb.edu.co/bitstream/20.500.12585/12584/4/finances-paper4.pdf.txt6831f44596e5f7d396bda281c1250394MD54THUMBNAILfinances-paper4.pdf.jpgfinances-paper4.pdf.jpgGenerated Thumbnailimage/jpeg5771https://repositorio.utb.edu.co/bitstream/20.500.12585/12584/5/finances-paper4.pdf.jpgfdff0c96246960fa19ce10c7d2ceca1eMD5520.500.12585/12584oai:repositorio.utb.edu.co:20.500.12585/125842023-12-07 00:29:43.256Repositorio Institucional UTBrepositorioutb@utb.edu.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 |