Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation

By Sérgio Moro, Paulo Cortez, Paulo Rita. Expert systems with applications, 2015

305 citations recorded by the source corpus.

Subfields: Information Systems, Management Information Systems, Marketing

View Paper: https://www.semanticscholar.org/paper/45ba6f80b57d741e38dc02f4c2f21be024e95df6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Sérgio Moro
2. Paulo Rita
3. Hossein Hassani
4. Paulo Cortez
5. Maedeh Taj Mazinani
6. Stephane Unger
7. Alexandra Amado
8. Longbing Cao

In Management Information Systems

1. Paulo Cortez
2. Paulo Rita
3. Longbing Cao
4. Sérgio Moro

In Marketing

1. Paulo Rita
2. Sérgio Moro
3. Alexandra Amado
4. Paulo Cortez
5. Longbing Cao
6. Dana Pessach
7. Erez Shmueli

Citations in the corpus

• Text Mining in Big Data Analytics (2020, background): “[202] performed topic detection on 219 articles between 2002 and 2013 through text mining when detecting terms pertaining to business intelligence in banking literature.”
• Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis (2018, methodology): “Following the approach of Delen nd Crossland (2008), this value was set to half of the terms conidered, given that the requirement to run the LDA is at least two opics (Moro et al., 2015a).”
• AI in Finance: Challenges, Techniques, and Opportunities (2021, background): “, predicting the trend and movements of price, market index and exchange rate; extracting and representing keywords, phrases, and expressions in a social and virtual community about manipulating a commodity price; predicting the market sentiment, reputation and confidence on a product or service; an”