{'fraction_non_alphanumeric': 0.025869037995149554, 'fraction_numerical': 0.0016168148746968471, 'mean_word_length': 5.098522167487685, '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}}
A Comparative Review of Text Mining & Related Technologies

Text mining has become an established discipline in both research and business intelligence. It refers commonly to the method of extracting interesting information and knowledge from unstructured text. Society's future will be closely connected to handling large amount of data. Information may be available in various ways, either freely on the Web or on social networks. Text mining is a multi-disciplinary field in view of Data Mining, Computational Linguistics, Artificial Intelligence and Machine Learning, Statistics, Databases, Library and Information Sciences, and actually the new field of Big Data. Some of these disciplines will be compared based on the goals, data, algorithms, techniques and the tools they use, as well as the their outcome. All these subjects are similar, which is based on two fundamental facts: (1) all of them develop methods and procedures to process data, and (2) any data processing algorithm or procedure may belong to some or even all. The differences are in their perspectives. This difference in perspectives does not affect the procedures but it does affect the choice of them and, even more so, interpretation of concepts and results.