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Cognitive Theories: Schachter-Singer Theory of Emotion01:20

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Related Experiment Video

Updated: May 19, 2026

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

Rule-based and lightly supervised methods to predict emotions in suicide notes.

Ted Pedersen1

  • 1Department of Computer Science, University of Minnesota, Duluth, MN, 55812, USA.

Biomedical Informatics Insights
|August 11, 2012
PubMed
Summary
This summary is machine-generated.

The Duluth systems used rule-based and automated methods for sentiment analysis in the i2b2 challenge. The best system, using manual corpus analysis, achieved a median F-measure of 0.45.

Keywords:
rule-basedsentiment classificationsuicide notes

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Artificial Intelligence-Based System for Detecting Attention Levels in Students
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Artificial Intelligence-Based System for Detecting Attention Levels in Students

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Last Updated: May 19, 2026

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Published on: December 15, 2023

Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Machine Learning

Background:

  • The i2b2/VA/Cincinnati Children's 2011 Challenge focused on sentiment analysis.
  • Developing effective systems for clinical text sentiment analysis is crucial.

Purpose of the Study:

  • To describe the Duluth systems developed for the Sentiment Analysis track of the i2b2/VA/Cincinnati Children's 2011 Challenge.
  • To evaluate the performance of different system configurations.

Main Methods:

  • A rule-based system was created using manual corpus analysis and measures of association to identify significant n-grams.
  • An automated system was developed by extracting the most frequent bigrams unique to specific emotions.
  • A combined system was formed by taking the union of the rule-based and automated approaches.

Main Results:

  • The rule-based system achieved an F-measure of 0.45, performing in the median range.
  • The automated system attained an F-measure of 0.36.
  • The combined system reached an F-measure of 0.44.

Conclusions:

  • Rule-based approaches derived from corpus analysis can yield competitive results in sentiment analysis.
  • Combining rule-based and automated methods offers a potential strategy for improving performance.
  • Further research into n-gram identification and automated feature extraction is warranted for clinical sentiment analysis.