Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records
Table 3: Results of the clinical sentiment extraction task.
['[BOLD] Model', '[BOLD] Domain', '[BOLD] Pos P', '[BOLD] Pos R', '[BOLD] Pos F1', '[BOLD] Neg P', '[BOLD] Neg R', '[BOLD] Neg F1', '[BOLD] Neu P', '[BOLD] Neu R', '[BOLD] Neu F1']
The ‘All’ row for each model configuration was computed by averaging the scores of the sentiment models for each risk factor domain. Applying the Pattern sentiment lexicon to our test corpus showed a strong trend towards underclassification of positive and negative examples, which led to poor recall scores while maintaining moderate precision. Neutral examples, however, were correctly classified significantly more often. This confirms that many of the most informative words in terms of clinical sentiment (e.g. ‘hallucination’, ‘depressed’, ‘employed’, etc.) do not hold significance in general-domain sentiment and are therefore not part of the Pattern lexicon.