Current approaches to sentiment analysis assume that the sole discourse function of sentiment-bearing texts is expressivity. However, the persuasive discourse function also utilises expressive language. In this work, we present the results of training supervised classifiers on a new corpus of clinical texts that contain documents with an expressive discourse function, and we test the learned models on a subset of the same corpus containing persuasive texts. The results of this indicate that despite the difference in discourse function, the learned models perform favourably.
|Publication status||Published - 2012|
|Event||Workshop on Computational Approaches to Subjectivity and Sentiment Analysis - Seogwipo, Korea, Republic of|
Duration: 12 Jul 2012 → 12 Jul 2012
|Workshop||Workshop on Computational Approaches to Subjectivity and Sentiment Analysis|
|Country||Korea, Republic of|
|Period||12/07/12 → 12/07/12|
Bibliographical noteThe full text is available from: http://aclweb.org/anthology/W/W12/W12-3712.pdf
Smith, P., & Lee, M. (2012). Cross-discourse Development of Supervised Sentiment Analysis in the Clinical Domain. Paper presented at Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, Seogwipo, Korea, Republic of.