Dataset: Covid Twitter Emotion Analysis

About this Dataset

Covid Twitter Emotion Analysis

Context

Twitter data was collected using Twitter’s Application Programming Interface(API) and Tweepy, a python library to access the twitter API. Certain keywords related to COVID’19 like Coronavirus, ncov, Wuhan, China, Covid-19, Epidemic, Pandemic, SocialDistancing, etc. were used to collect the tweets. Only the tweets that were in English and the ones that had a geo-tag were collected. During the exploratory data analysis, we noticed that a number of tweets consisted of only certain words and not proper sentences and analyzing the emotion of such tweets might not give us a proper overview of the emotions. Thus, only the tweets with at least 6 words in them were used. This significantly reduced the number of tweets collected. Finally, we had over 1 million tweets over the span of February, March, April, May, and June. The tweets were then further processed to remove all the HTML text, ‘@’ mentions, URL links, and #hashtags.

Content

The data was analyzed using a machine learning model and tweets were categorized into various emotions. The dataset provides the count of tweets per country per emotion for 5 months.

Acknowledgements

Matta, Nikhil (2020), “Covid Twitter Emotion Analysis Data”, Mendeley Data, V1, doi: 10.17632/47hy8yyky5.1

Tables

Emotiongroupsapril

@kaggle.saurabhshahane_covid_twitter_emotion_analysis.emotiongroupsapril

17.18 KB
385 rows
13 columns


CREATE TABLE emotiongroupsapril (
  "unnamed_0" BIGINT,
  "emotion_str" VARCHAR,
  "country" VARCHAR,
  "tweet" BIGINT,
  "name" VARCHAR,
  "alpha_3" VARCHAR,
  "country_code" BIGINT,
  "country_880c2e" VARCHAR,
  "users_in_million" DOUBLE,
  "source" VARCHAR,
  "total_tweets" DOUBLE,
  "tweetpm" DOUBLE,
  "date" VARCHAR
);

Emotiongroupsfeb

@kaggle.saurabhshahane_covid_twitter_emotion_analysis.emotiongroupsfeb

17.13 KB
381 rows
13 columns


CREATE TABLE emotiongroupsfeb (
  "unnamed_0" BIGINT,
  "emotion_str" VARCHAR,
  "country" VARCHAR,
  "tweet" BIGINT,
  "name" VARCHAR,
  "alpha_3" VARCHAR,
  "country_code" BIGINT,
  "country_880c2e" VARCHAR,
  "users_in_million" DOUBLE,
  "source" VARCHAR,
  "total_tweets" DOUBLE,
  "tweetpm" DOUBLE,
  "date" VARCHAR
);

Emotiongroupsjune

@kaggle.saurabhshahane_covid_twitter_emotion_analysis.emotiongroupsjune

15.64 KB
279 rows
13 columns


CREATE TABLE emotiongroupsjune (
  "unnamed_0" BIGINT,
  "emotion_str" VARCHAR,
  "country" VARCHAR,
  "tweet" BIGINT,
  "date" VARCHAR,
  "name" VARCHAR,
  "alpha_3" VARCHAR,
  "country_code" BIGINT,
  "country_880c2e" VARCHAR,
  "users_in_million" DOUBLE,
  "source" VARCHAR,
  "total_tweets" DOUBLE,
  "tweetpm" DOUBLE
);

Emotiongroupsmarch

@kaggle.saurabhshahane_covid_twitter_emotion_analysis.emotiongroupsmarch

16.81 KB
388 rows
13 columns


CREATE TABLE emotiongroupsmarch (
  "unnamed_0" BIGINT,
  "emotion_str" VARCHAR,
  "country" VARCHAR,
  "tweet" BIGINT,
  "name" VARCHAR,
  "alpha_3" VARCHAR,
  "country_code" BIGINT,
  "country_880c2e" VARCHAR,
  "users_in_million" DOUBLE,
  "source" VARCHAR,
  "total_tweets" DOUBLE,
  "tweetpm" DOUBLE,
  "date" VARCHAR
);

Emotiongroupsmay

@kaggle.saurabhshahane_covid_twitter_emotion_analysis.emotiongroupsmay

17.11 KB
369 rows
13 columns


CREATE TABLE emotiongroupsmay (
  "unnamed_0" BIGINT,
  "emotion_str" VARCHAR,
  "country" VARCHAR,
  "tweet" BIGINT,
  "name" VARCHAR,
  "alpha_3" VARCHAR,
  "country_code" BIGINT,
  "country_880c2e" VARCHAR,
  "users_in_million" DOUBLE,
  "source" VARCHAR,
  "total_tweets" DOUBLE,
  "tweetpm" DOUBLE,
  "date" VARCHAR
);