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Table 3: Results
['Content Classifier', 'Content P', 'Content R', 'Content F1']
Performance results of the classifiers using content-based features suggests a remarkable improvement for CRF over the rest of the classifiers, implying that CRF benefits from the use of the sequence of tweets preceding each tweet as context to enrich the input to the classifier. This is especially true when we look at precision, where CRF performs substantially better than the rest. Only the Naive Bayes classifier performs better in terms of recall, however, it performs poorly in terms of precision. As a result, CRF balances precision and recall in a clearly better way, outperforming all the other classifiers in terms of the F1 score.