{'fraction_non_alphanumeric': 0.030395136778115502, 'fraction_numerical': 0.005065856129685917, 'mean_word_length': 4.880952380952381, '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}}
Cross-Genre Age and Gender Identification in Social Media

This paper gives a brief description on the methods adopted for the task of author-profiling as part of the competition PAN 2016 [1]. Author profiling is the task of predicting the author’s age and gender from his/her writing. In this paper, we follow a two-level ensemble approach to tackle the cross-genre author profiling task where training documents and testing documents are from different genres. We use the softvoting approach to build the classification ensemble. To include various feature sets, we first train logistic regression models using the extracted word n-gram, character n-gram, and part-of-speech n-gram features for each genre. We then ensemble single-genre predictive models trained on the blog, social media and Twitter data sources, to build our multi-genre ensemble approach. The experimental results indicate that our approach performs well in both single-genre and cross-genre author profiling tasks.