{'fraction_non_alphanumeric': 0.021791767554479417, 'fraction_numerical': 0.004842615012106538, 'mean_word_length': 5.2180451127819545, '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}}
Garbage in, garbage out: Impact of sequence matching based text cleaning and phrase identification on unsupervised text mining

In 2006, Daim et al. published the highly cited paper on forecasting emerging technologies with bibliometrics and patent analysis. In the paper, scientific publications and patent were used as a numerical input to for example system dynamic models or scenarios –elaborating on the current state and trend of technological development as a year-to-year indicator value. By forcing the indicator to the well-known growth models the analyst also had an indication of future development. This approach of quantifying instances of publication of patenting is to a significant extent valid in producing a “how much” indicator, but yields far less an indication on the “what” of technological development.