{'fraction_non_alphanumeric': 0.021377672209026127, 'fraction_numerical': 0.0, 'mean_word_length': 5.59375, '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}}
LSA for Mining Hidden Information in Action Game Semantics

This paper describes the application of Latent Semantic Analysis to the term-document matrices that result from modeling an action game. Innovative solutions to address challenges like the definition of "words" and "documents" in the dynamic and complex domain of action games are proposed, and interesting, previously unknown semantic information is extracted.