{'fraction_non_alphanumeric': 0.01540436456996149, 'fraction_numerical': 0.012836970474967908, 'mean_word_length': 5.24, '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}}
Quarantine Quibbles: A Sentiment Analysis of COVID-19 Tweets

With the advent of the COVID-19 pandemic, people have flocked to social media in order to stage their thoughts surrounding these unusual circumstances. This paper aims to uncover public sentiment regarding the novel coronavirus pandemic on the microblogging platform Twitter. This is done through a proposed algorithm that builds off of existing aspect-based sentiment analysis approaches and opts for a Naïve-Bayes route to classify existing Tweets that have been atomized into n-grams. This research concludes that overall sentiment regarding the COVID-19 outbreak over July 2020 is a combination of pessimism and dejection as our quarantine denizens take to their online platforms in airing their polemic opinions.