{'fraction_non_alphanumeric': 0.014331210191082803, 'fraction_numerical': 0.0, 'mean_word_length': 5.418367346938775, '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}}
Trend Mining with Semantic-Based Learning

Mining trends by analyzing text streams could enhance the standard trend analysis based on numeric data. The use of qualitative information in the process of trend recognition, in addition to that of quantitative data, requires new analysis techniques. Since Semantic Web enables the appropriate and advantageous formalization of knowledge, we propose to include formalized expert knowledge in the process of trend recognition. In this preliminary work, we introduce our approach based on Semantic Web technologies combined with Data Mining methods for mining trends in a given domain.