Análisis de sentimientos en noticias financieras y económicas hondureñas usando web scraping e inteligencia artificial
Loading...
Date
2025-01-01
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad Tecnológica Centroamericana UNITEC
Abstract
El presente trabajo tiene como objetivo realizar un análisis de sentimientos en noticias relacionadas con finanzas, economía y estabilidad financiera, obtenidas de diversas fuentes en línea mediante técnicas de web scraping. Este enfoque mixto, que combina análisis cualitativo y cuantitativo, permite clasificar y comprender grandes volúmenes de datos no estructurados; Para el análisis de sentimientos, se utiliza un diccionario temático especializado en el ámbito económico y financiero, este estudio, automatizado y desarrollado en Python, facilita la recolección y procesamiento de datos, permitiendo identificar opiniones y emociones en los textos analizados, lo que ayuda a comprender la percepción pública sobre temas económicos clave y brinda información valiosa para la toma de decisiones. Los resultados obtenidos muestran que la percepción varía significativamente según la fuente de las noticias, el tema abordado, los eventos económicos y climatológicos registrados, destacándose las emociones relacionadas con la economía y finanzas, este tipo de análisis es crucial para las instituciones financieras, ya que les permite monitorear cómo los medios presentan la información y detectar posibles interpretaciones erróneas; por ello se recomienda a las instituciones financieras utilizar estas herramientas de análisis para identificar patrones en las noticias que afectan la toma de decisiones estratégicas, lo que les permitirá responder de manera proactiva a las dinámicas económicas y financieras.
The objective of this work is to carry out a sentiment analysis on news related to finance, economy and financial stability, obtained from various online sources using web scraping techniques. This mixed approach, which combines qualitative and quantitative analysis, makes it possible to classify and understand large volumes of unstructured data; For sentiment analysis, a dictionary specialized in the economic and financial field is used. This study, automated and developed in Python, facilitates the collection and processing of data, allowing opinions and emotions to be identified in the analyzed texts, which helps to understand public perception on key economic issues and provides valuable information for decision making. The results obtained show that perception varies significantly depending on the source of the news, the topic addressed, the economic and climatological events recorded, highlighting the emotions related to the economy and finances, this type of analysis is crucial for financial institutions, since allows them to monitor how the media presents information and detect possible misinterpretations; Therefore, financial institutions are recommended to use these analysis tools to identify patterns in the news that affect strategic decision-making, which will allow them to respond proactively to economic and financial dynamics.
The objective of this work is to carry out a sentiment analysis on news related to finance, economy and financial stability, obtained from various online sources using web scraping techniques. This mixed approach, which combines qualitative and quantitative analysis, makes it possible to classify and understand large volumes of unstructured data; For sentiment analysis, a dictionary specialized in the economic and financial field is used. This study, automated and developed in Python, facilitates the collection and processing of data, allowing opinions and emotions to be identified in the analyzed texts, which helps to understand public perception on key economic issues and provides valuable information for decision making. The results obtained show that perception varies significantly depending on the source of the news, the topic addressed, the economic and climatological events recorded, highlighting the emotions related to the economy and finances, this type of analysis is crucial for financial institutions, since allows them to monitor how the media presents information and detect possible misinterpretations; Therefore, financial institutions are recommended to use these analysis tools to identify patterns in the news that affect strategic decision-making, which will allow them to respond proactively to economic and financial dynamics.
Keywords
Análisis de sentimientos, Aprendizaje automático, Connotación, Polaridad, Web Scraping