Search engines in the use of financial sentiment analysis
Tutor/a - Director/a
Torra i Porras, Salvador
Estudiant
Coronado Montoro, Ramon
Tipus de document
Treball Final de Grau
Data
2024
rights
Accés obert
Editorial
Universitat de Barcelona
Titulacions
UPCommons
Resum
Financial market predictions often rely on historical and numerical data, but recent advancements in large language models encourage the use
of alternative datasets like financial news text. However, this method- ology often faces limitations due to the scarcity of extensive datasets
that combine both quantitative and qualitative sentiment analyses. To address this gap, we used the Bing Search API to build a dataset com-
prising over 100.000 financial news articles from more than 90 web-sites. Our work aims to illuminate the process of building a dataset using search engines, demonstrating that the use of keywords to collect ”custom” data from the vast Internet is an effective alternative for data collection. We evaluated the dataset using a sentiment index, which we later compared with the S&P 500 stock index. We concluded that while news sentiment may not immediately refflect price variations, it can effectively indicate broader market trends.
Professorat participant
- Torra i Porras, Salvador