{'fraction_non_alphanumeric': 0.017991004497751123, 'fraction_numerical': 0.0, 'mean_word_length': 4.911504424778761, '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}}
'Meme'tic engineering to classify Twitter lingo

Text memes have become a popular form of communicating ideologies on social networking websites. Some of these have led to drastic consequences in the society and has also influenced lives of several people. Text memes are pieces of text that are copied and spread rapidly by internet users. The sentiment can refer to two different types: emotions and opinions. These opinions can be divided into three classes: positive, negative and neutral. The inference obtained from this paper are the various categories of text memes circulated amongst users and an analysis of the over-all reaction or sentiment of the public.