Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records
Table 4: Hyperparameters for sentiment model.
['[BOLD] Parameter', '[BOLD] Value']
Due to the relatively small amount of labeled training data, our proposed model architecture is designed to prevent overfitting by using a restricted view of the training data via a high rate of dropout in the hidden layers. Additionally, we use two hidden layers to extract a more abstracted form of the input. Additionally, because neutral sentiment is much broader in scope and has fewer training examples, resulting in covariate shift, we compute a threshold for classifying positive and negative sentiment using the formula min=avg(sim)+α*σ(sim), where σ is standard deviation and α is a constant, which we set to 0.2. If a given test sentence does not have positive or negative outputs that exceed this threshold, the sentence is classified as neutral even if neutral is not the maximal output.