COVID Analysis: Data Sharing

Our dataset was created from a couple dozen smaller datasets gathered from IEEE. The dataset it self only contains ID's to reference tweets, and not the actual tweet content, so we had to create a script to go through and hydrate them. Once this was done, the dataset was ready to have sentiment analysis performed on it.

The resulting dataset has 258,800 tweets, and contains the following fields:

coordinates created_at hashtags media urls favorite_count id in_reply_to_screen_name in_reply_to_status_id in_reply_to_user_id lang place possibly_sensitive retweet_count retweet_id retweet_screen_name source text tweet_url user_created_at user_screen_name user_default_profile_image user_description user_favourites_count user_followers_count user_friends_count user_listed_count user_location user_name user_screen_name user_statuses_count user_time_zone user_urls user_verified

For the most part, our group only used the user_name, text, user_location, and created_at fields, as they were most pertinent to our issue.

An example row look like this:
36.77943,-1.29889 | Wed Apr 01 09:01:18 +0000 2020 | corona coronavirus covid stayhome | | 0 | 1.24527503095654E+018 | en | Nairobi, Kenya | true | 0 | Instagram | Wednesday 1st of April I wanna wish you guys good healthy and strong pockets keep on fights corona and be safe!!!! #corona #coronavirus #covid #stayhome @ Mombasa Island Kenya | | | Sun Jul 21 13:40:52 +0000 2013 | bandanahmiqassa | false | Official Twitter Account Of Musical Artiste MOHAMMED MIQASSA | East Africa 🇹🇿🇰🇪 | United Kingdom | 🇬🇧 | 10777 | 6727 | 3 | 15 | EAST AFRICA, WORLDWIDE | NDANAH MIQASSA | bandanahmiqassa 23038 | | false

Our dataset can be downloaded from this link.

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