{'fraction_non_alphanumeric': 0.015197568389057751, 'fraction_numerical': 0.0, 'mean_word_length': 5.398058252427185, '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}}
Multiclass Sentiment Prediction using Yelp Business Reviews

Many e-commerce and related sites allow text reviews, which provide much more detail than the quantitative metric of star ratings. However, star ratings provide insight on a establishment or product very quickly. Seeding new e-commerce sites with this quick quantitative information from pre-existing text reviews scattered throughout the Internet would greatly improve the information provided about an item or establishment. Is it possible to translate the detail of text reviews into the quick usefulness of a numerical rating? We hope to explore this through multiple deep learning techniques.