{'fraction_non_alphanumeric': 0.013333333333333334, 'fraction_numerical': 0.0, 'mean_word_length': 5.261904761904762, '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}}
Emotion Analysis of Internet Chat

We present a system for Emotion Analysis of Instant Messages (IM). Using Instance Based classifier we have shown that our system can outperform similar systems in the IM domain. Tagged instant messages and elaborate feature engineering can help a lot in increasing the performance of text classification of unstructured, ungrammatical text. The impact of class imbalance on classification has been studied and demonstration has been made of how undersampling can help mitigate this problem.