Data
Texas-Winter-Storm-2021-Tweets

Texas-Winter-Storm-2021-Tweets

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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Context Winter Storm Uri in February 2021 caused havoc across the United States and specifically to Texas involving mass power outages, water and food shortages, and dangerous weather conditions. This dataset consists of 23K+ tweets during the crisis week. Data is filtered to mostly include the tweets from influencers (users having more than 5000 followers) however there is a small subset of tweets from other users as well. My notebook - https://www.kaggle.com/rajsengo/eda-texas-winterstrom-2021-tweets Acknowledgements https://www.kaggle.com/gpreda/pfizer-vaccine-tweets - For the inspiration https://github.com/dataquestio/twitter-scrape - Reference utility to scrape twitter Inspiration Apply NLP techniques to undestand user sentiments about the crisis management

14 features

id_strnumeric23358 unique values
0 missing
user_namestring11851 unique values
0 missing
textstring11482 unique values
41 missing
hashtagsstring918 unique values
21024 missing
createdstring13532 unique values
0 missing
user_followersnumeric10705 unique values
0 missing
user_friendsnumeric9474 unique values
0 missing
user_favoritesnumeric19525 unique values
0 missing
expanded_urlstring3783 unique values
17704 missing
user_descriptionstring11544 unique values
481 missing
user_createdstring11849 unique values
0 missing
user_locationstring5043 unique values
4170 missing
sourcestring162 unique values
8 missing
usr_mentionsstring3267 unique values
10271 missing

19 properties

23358
Number of instances (rows) of the dataset.
14
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
53699
Number of missing values in the dataset.
23197
Number of instances with at least one value missing.
4
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
28.57
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
99.31
Percentage of instances having missing values.
Average class difference between consecutive instances.
16.42
Percentage of missing values.

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