Toward Automatic Understanding of the Function of Affective Language in Support Groups
Table 8: Precision, Recall and F1 for Sentence-level classification of subjectivity-adjusted corpus
['[EMPTY]', '[ITALIC] P', '[ITALIC] R', '[ITALIC] F']
One approach to re-labelling of data is to take advantage of coarser levels of annotation: that of subjectivity. Is it possible to at least distinguish which sentences are objective, and could be labelled as FACT? We ran the model on all sentences of the corpus in order to assess their subjectivity. Any sentence with a subjectivity likelihood of <0.7 (chosen following manual assessment) we consider to be objective; the remainder are subjective. To attempt to eliminate the confusion with the FACT category we eliminated objective sentences from sets previously labelled with the 4 subjective categories. In addition, because this “confusion” can be bi-directional, we also removed any subjective sentences which were previously FACT. This MedSenti-sent-subj set consists of 4147 sentences.