{'fraction_non_alphanumeric': 0.014675052410901468, 'fraction_numerical': 0.0, 'mean_word_length': 5.121794871794871, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Text Classification Performance: Is the Sample Size the Only Factor to be Considered?

The use of text mining and supervised machine learning algorithms on biomedical databases has become increasingly common. However, a question remains: How much data must be annotated to create a suitable training set for a machine learning classifier? In prior research with active learning in medical text classification, we found evidence that not only sample size but also some of the intrinsic characteristics of the texts being analyzed-such as the size of the vocabulary and the length of a document-may also influence the resulting classifier's performance. This study is an attempt to create a regression model to predict performance based on sample size and other text features. While the model needs to be trained on existing datasets, we believe it is feasible to predict performance without obtaining annotations from new datasets once the model is built.