Overview of FinancES 2023: Financial Targeted Sentiment Analysis in Spanish

• Marín, María José ^[1] ; Valencia García, Rafael ; García-Díaz, José Antonio ; Almela Sánchez-Lafuente, Ángela ; García-Sánchez, Francisco ; Alcaraz Mármol, Gema
  1. [1] Universidad de Murcia

     Universidad de Murcia

     Murcia, España

     
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 71, 2023, págs. 417-423
• Idioma: inglés
• Títulos paralelos:
  • Resumen de FinancES 2023: Análisis de Sentimiento Dirigido en Español sobre Finanzas
• Enlaces
  • Texto completo
• Resumen
  • Multiple

    Este artículo resume la tarea FinancES 2023, organizada en el taller IberLEF 2023, dentro del marco de la 39ª Conferencia Internacional de la Sociedad Española de Procesamiento del Lenguaje Natural (SEPLN 2023). El objetivo de esta tarea es mejorar la materia de la minería de opiniones en español dentro del ámbito financiero realizando el análisis de sentimientos desde distintos puntos de vista. En concreto, se proponen y estudian dos tareas que son evaluadas de forma independiente. La primera tarea consiste en (i) identificar el actor principal asociado a una noticia financiera, y (ii) el sentimiento expresado hacia dicho actor. La segunda tarea consiste en determinar el sentimiento de la noticia (i) hacia otras empresas (i.e., otros agentes económicos), y (ii) hacia los consumidores (i.e., la sociedad). El ranking incluye los resultados de 10 equipos diferentes que proponen enfoques novedosos, en su mayoría basados en Transformers y modelos generativos del lenguaje.

  • English

    This paper presents the FinancES 2023 shared task, organized in the IberLEF 2023 workshop, within the framework of the 39th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2023). The aim of this task is to extend the challenge of sentiment analysis in Spanish to the financial domain, in order to extract the sentiment that a piece of financial information can have for several actors, including the main economic target (i.e., the specific company or asset where the economic fact applies), other companies (i.e., the entities producing the goods and services that others consume) and consumers (i.e., households/individuals). Specifically, two tasks are proposed and evaluated separately. One to identify the main target and to determine the sentiment polarity towards such target, and a second task to assess the sentiment towards both other companies and consumers. The ranking includes results for 10 different teams proposing novel approaches, mostly based on Transformers and generative language models.

• Referencias bibliográficas
  • Bowles, S. 2003. Microeconomics: behavior, institutions, and evolution. Princeton University Press.
  • Cañete, J., G. Chaperon, R. Fuentes, J.-H. Ho, H. Kang, and J. Pérez. 2020. Spanish pre-trained bert model and evaluation data. Pml4dc at...
  • Cuadrado, J., E. Martinez, J. C. MartinezSantos, and E. Puertas. 2023. Team UTB-NLP at FinancES 2023: Financial Targeted Sentiment Analysis...
  • Goodell, J. W., S. Kumar, P. Rao, and S. Verma. 2022. Emotions and stock market anomalies: a systematic review. Journal of Behavioral and...
  • Hasan, M. M., J. Popp, and J. Oláh. 2020. Current landscape and influence of big data on finance. Journal of Big Data, 7(1):1–17.
  • Honnibal, M. and I. Montani. 2017. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental...
  • Jiménez-Zafra, S. M., D. García-Baena, M. ángel García-Cumbreras, and M. García-Vega. 2023. SINAI at FinancESIberLEF2023: Evaluating Popular Tools...
  • Jiménez-Zafra, S. M., F. Rangel, and M. Montes-y Gómez. 2023. Overview of IberLEF 2023: Natural Language Processing Challenges for Spanish...
  • Milne, A. and M. Chisholm. 2013. The prospects for common financial language in wholesale financial services. SSRN Electronic Journal.
  • Nemes, L. and A. Kiss. 2021. Prediction of stock values changes using sentiment analysis of stock news headlines. Journal of Information and...
  • Pan, R., J. A. García-Díaz, F. GarciaSanchez, and R. Valencia-García. 2023. Evaluation of transformer models for financial targeted sentiment...
  • Porta-Zamorano, J., Y. Torterolo, and A. Moreno-Sandoval. 2023. LLI-UAM Team at FinancES 2023: Noise, Data Augmentation and Hallucinations....
  • Rodríguez-García, M. A., A. RiañoMartínez, D. Roldán- álvarez, and S. Montalvo-Herranz. 2023. URJC-Team at FinancES 2023: Financial Targeted Sentiment...
  • Salas-Zárate, M. d. P. and M. A. ParedesValverde. 2023. Team ITST at FinancES 2023: A Psycholinguistic-based Sentiment Analysis Approach....
  • Tausczik, Y. R. and J. W. Pennebaker. 2010. The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language...
  • Thin, D. V., D. N. Ba, D. N. Hao, and N. L.-T. Nguyen. 2023. ABCD Team at FinancES 2023: An Unified Generative Framework for the Financial...
  • Xue, L., N. Constant, A. Roberts, M. Kale, R. Al-Rfou, A. Siddhant, A. Barua, and C. Raffel. 2021. mt5: A massively multilingual pre-trained...