The Link Between Emotions And Actionability by Kaggle | Ecommerce and Consumer Trends

About this Dataset

The Link Between Emotions And Actionability

The Link Between Emotions and Actionability

Finding a link between emotions and actionability tweets, Rana. 2022

This dataset aims to find actionable tweets tweeted about the City of Calgary that can pinpoint problems they should act upon.
The problem arises because they could not find any efficient methods to filter these actionable tweets. The current method used by the City of Calgary to find problems they can act upon requires a lot of human interaction and time.

Instead of spending all these resources to find problems, They believe these resources are better spent solving the problems. They
believe that tweets conveying a fearful emotion are more likely to be actionable.

The authors found a strong correlation of 0.694 between the number of fearful tweets per month to the number of actionable
tweets in a month. Additionally, the authors found out that a tweet that was labeled fearful has a 0.5 probability to be actionable.

Tables

Data Normalized

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.data_normalized

368.07 KB
2000 rows
16 columns


CREATE TABLE data_normalized (
  "unnamed_0_2" BIGINT,
  "unnamed_0_1" BIGINT,
  "unnamed_0" BIGINT,
  "id" DOUBLE,
  "author_id" DOUBLE,
  "text" VARCHAR,
  "hashtags" VARCHAR,
  "created_at" VARCHAR,
  "geo" VARCHAR,
  "like_count" BIGINT,
  "quote_count" BIGINT,
  "reply_count" BIGINT,
  "retweet_count" BIGINT,
  "possibly_sensitive" VARCHAR,
  "actionability" VARCHAR,
  "actionability_2" VARCHAR
);

Data Normlized

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.data_normlized

368.07 KB
2000 rows
16 columns


CREATE TABLE data_normlized (
  "unnamed_0_2" BIGINT,
  "unnamed_0_1" BIGINT,
  "unnamed_0" BIGINT,
  "id" DOUBLE,
  "author_id" DOUBLE,
  "text" VARCHAR,
  "hashtags" VARCHAR,
  "created_at" VARCHAR,
  "geo" VARCHAR,
  "like_count" BIGINT,
  "quote_count" BIGINT,
  "reply_count" BIGINT,
  "retweet_count" BIGINT,
  "possibly_sensitive" VARCHAR,
  "actionability" VARCHAR,
  "actionability_2" VARCHAR
);

Data Sample

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.data_sample

115.88 KB
300 rows
26 columns


CREATE TABLE data_sample (
  "unnamed_0_3" BIGINT,
  "unnamed_0_2" BIGINT,
  "unnamed_0" BIGINT,
  "level_0" BIGINT,
  "index" BIGINT,
  "unnamed_0_1" BIGINT,
  "unnamed_0_1_1" BIGINT,
  "id" DOUBLE,
  "author_id" DOUBLE,
  "text" VARCHAR,
  "hashtags" VARCHAR,
  "created_at" VARCHAR,
  "geo" VARCHAR,
  "like_count" BIGINT,
  "quote_count" BIGINT,
  "reply_count" BIGINT,
  "retweet_count" BIGINT,
  "possibly_sensitive" BOOLEAN,
  "text_normalized" VARCHAR,
  "cluster" BIGINT,
  "distance_from_level_1_centroid" DOUBLE,
  "sub_cluster" BIGINT,
  "distance_from_level_2_centroid" DOUBLE,
  "emotion" VARCHAR,
  "emotion_score" DOUBLE,
  "actionability" VARCHAR
);

Example

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.example

47.99 KB
301 rows
6 columns


CREATE TABLE example (
  "text" VARCHAR,
  "reeshan_eval" VARCHAR,
  "reeshad_actionable" VARCHAR,
  "divyansh_eval" VARCHAR,
  "divyansh_actionable" VARCHAR,
  "kirtan" VARCHAR
);

Example2

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.example2

16.82 KB
111 rows
7 columns


CREATE TABLE example2 (
  "text" VARCHAR,
  "kirtan_eval" VARCHAR,
  "kirtan_actionable" VARCHAR,
  "reeshad_eval" VARCHAR,
  "reeshad_actionable" VARCHAR,
  "divyansh_eval" VARCHAR,
  "divyansh_actionable" VARCHAR
);

Example3

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.example3

14.74 KB
56 rows
6 columns


CREATE TABLE example3 (
  "text" VARCHAR,
  "kirtan" VARCHAR,
  "reeshad_eval" VARCHAR,
  "reeshad_actionable" VARCHAR,
  "divyansh_eval" VARCHAR,
  "divyansh_actionable" VARCHAR
);

Example Normalized

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.example_normalized

76.19 KB
468 rows
11 columns


CREATE TABLE example_normalized (
  "unnamed_0" BIGINT,
  "text" VARCHAR,
  "kirtan_eval" VARCHAR,
  "kirtan_actionable" VARCHAR,
  "reeshad_eval" VARCHAR,
  "reeshad_actionable" VARCHAR,
  "divyansh_eval" VARCHAR,
  "divyansh_actionable" VARCHAR,
  "reeshan_eval" VARCHAR,
  "emotion" VARCHAR,
  "emotion_score" DOUBLE
);

News Tweets

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.news_tweets

6.58 MB
4000 rows
2 columns


CREATE TABLE news_tweets (
  "unnamed_0" BIGINT,
  "train" VARCHAR
);

Park Tweet File

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.park_tweet_file

2.81 MB
10641 rows
24 columns


CREATE TABLE park_tweet_file (
  "unnamed_0_2" BIGINT,
  "unnamed_0" BIGINT,
  "level_0" BIGINT,
  "index" BIGINT,
  "unnamed_0_1" BIGINT,
  "unnamed_0_1_1" BIGINT,
  "id" DOUBLE,
  "author_id" DOUBLE,
  "text" VARCHAR,
  "hashtags" VARCHAR,
  "created_at" VARCHAR,
  "geo" VARCHAR,
  "like_count" BIGINT,
  "quote_count" BIGINT,
  "reply_count" BIGINT,
  "retweet_count" BIGINT,
  "possibly_sensitive" BOOLEAN,
  "text_normalized" VARCHAR,
  "cluster" BIGINT,
  "distance_from_level_1_centroid" DOUBLE,
  "sub_cluster" BIGINT,
  "distance_from_level_2_centroid" DOUBLE,
  "emotion" VARCHAR,
  "emotion_score" DOUBLE
);

Sample 1000–1 Reeshad

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.sample_1000_1_reeshad

187.78 KB
1000 rows
14 columns


CREATE TABLE sample_1000_1_reeshad (
  "unnamed_0_1" BIGINT,
  "unnamed_0" BIGINT,
  "id" DOUBLE,
  "author_id" DOUBLE,
  "text" VARCHAR,
  "hashtags" VARCHAR,
  "created_at" VARCHAR,
  "geo" VARCHAR,
  "like_count" BIGINT,
  "quote_count" BIGINT,
  "reply_count" BIGINT,
  "retweet_count" BIGINT,
  "possibly_sensitive" VARCHAR,
  "actionability" VARCHAR
);

Sample 1000–1 Anita

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.sample_1000_1_anita

188.32 KB
1000 rows
15 columns


CREATE TABLE sample_1000_1_anita (
  "unnamed_0_1" BIGINT,
  "unnamed_0" BIGINT,
  "id" DOUBLE,
  "author_id" DOUBLE,
  "text" VARCHAR,
  "hashtags" VARCHAR,
  "created_at" VARCHAR,
  "geo" VARCHAR,
  "like_count" BIGINT,
  "quote_count" BIGINT,
  "reply_count" BIGINT,
  "retweet_count" BIGINT,
  "possibly_sensitive" BOOLEAN,
  "unnamed_13" VARCHAR,
  "actionability" VARCHAR
);

Sample 1000–2 Divyansh

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.sample_1000_2_divyansh

183.4 KB
1000 rows
14 columns


CREATE TABLE sample_1000_2_divyansh (
  "unnamed_0_1" BIGINT,
  "unnamed_0" BIGINT,
  "id" DOUBLE,
  "author_id" DOUBLE,
  "text" VARCHAR,
  "hashtags" VARCHAR,
  "created_at" VARCHAR,
  "geo" VARCHAR,
  "like_count" BIGINT,
  "quote_count" BIGINT,
  "reply_count" BIGINT,
  "retweet_count" BIGINT,
  "possibly_sensitive" BOOLEAN,
  "actionability" VARCHAR
);

Sample 1000–2 Kirtan

@kaggle.thedevastator_the_link_between_emotions_and_the_actionability.sample_1000_2_kirtan

185 KB
1000 rows
17 columns


CREATE TABLE sample_1000_2_kirtan (
  "unnamed_0_1" BIGINT,
  "unnamed_0" BIGINT,
  "id" DOUBLE,
  "author_id" DOUBLE,
  "text" VARCHAR,
  "hashtags" VARCHAR,
  "created_at" VARCHAR,
  "geo" VARCHAR,
  "like_count" BIGINT,
  "quote_count" BIGINT,
  "reply_count" BIGINT,
  "retweet_count" BIGINT,
  "possibly_sensitive" BOOLEAN,
  "unnamed_13" VARCHAR,
  "unnamed_14" VARCHAR,
  "unnamed_15" VARCHAR,
  "actionability" VARCHAR
);