What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
Table 1: Single-label Classification Results on EmoCT single-labeled version.
['Method', 'Accuracy', 'F1']
Single-label Classification We first attempt to do a single-label classification task based on the single-labeled version of EmoCT. We take the output of the [CLS] token and add a fully-connected layer, which is fine-tuned using the labeled training examples (BERT). We set the learning rate to be 10−5 and number of epochs to be 20. Besides, we also fine-tune with the MLM (masked language model) on 1,181,342 unlabeled tweets randomly selected from our crawled data, and then trained on EmoCT (BERT(ft)). As we can see, both models have competitive results on accuracy and F1, and BERT(ft) performs slightly better than BERT, so we take this model as our main model for analysis in later sections.