{'fraction_non_alphanumeric': 0.021406727828746176, 'fraction_numerical': 0.0, 'mean_word_length': 5.683673469387755, '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}}
POMon: From Simple Keyword Matching to Stream-Based Live Probabilistic Topic Matching

Public opinion monitoring is defined as story detection and matching with particular Internet news event. Traditional method used topic discovery and text matching techiques to get results. However, it has some limitations: hidden topic discovery and incorrect match ranking on big data. In this demonstration, we present the architecture evolution of public sentiment monitoring system (POMon) and ranking method of live probabilistic topic matching. Meanwhile, we discuss the evolution from simple keyword matching to stream-based live probabilistic topic matching.