Demo Data from EDBT 2026 / reviewers in the wild / expert
Clemens Nopp
dblp:26/10044
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational finance and economics · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › sentiment analysis
financial sentiment analysis |
0.2 | 1 | 2015 | Detecting Risks in the Banking System by Sentiment Analysis · EMNLP 2015 |
Natural language and speech › Information extraction and text analysis
sentiment analysis |
0.2 | 1 | 2015 | Detecting Risks in the Banking System by Sentiment Analysis · EMNLP 2015 |
Computational finance and economics › financial risk management
financial risk assessment |
0.2 | 1 | 2015 | Detecting Risks in the Banking System by Sentiment Analysis · EMNLP 2015 |
Methods — techniques the papers use, named apart from their topics
sentiment analysis · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Detecting Risks in the Banking System by Sentiment AnalysisabstractIn November 2014, the European Central Bank (ECB) started to directly supervise the largest banks in the Eurozone via the Single Supervisory Mechanism (SSM).While supervisory risk assessments are usually based on quantitative data and surveys, this work explores whether sentiment analysis is capable of measuring a bank's attitude and opinions towards risk by analyzing text data.For realizing this study, a collection consisting of more than 500 CEO letters and outlook sections extracted from bank annual reports is built up.Based on these data, two distinct experiments are conducted.The evaluations find promising opportunities, but also limitations for risk sentiment analysis in banking supervision.At the level of individual banks, predictions are relatively inaccurate.In contrast, the analysis of aggregated figures revealed strong and significant correlations between uncertainty or negativity in textual disclosures and the quantitative risk indicator's future evolution.Risk sentiment analysis should therefore rather be used for macroprudential analyses than for assessments of individual banks. Clemens Nopp, Allan Hanbury |
EMNLP | 1 |