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Clemens Nopp

dblp:26/10044 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › sentiment analysis
financial sentiment analysis
0.212015
Detecting Risks in the Banking System by Sentiment Analysis · EMNLP 2015
Natural language and speech › Information extraction and text analysis
sentiment analysis
0.212015
Detecting Risks in the Banking System by Sentiment Analysis · EMNLP 2015
Computational finance and economics › financial risk management
financial risk assessment
0.212015
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
YearPublicationVenuePosition
2015 Detecting Risks in the Banking System by Sentiment Analysis
abstract
In 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
EMNLP1