Name: YouTube Comments Analysis On Oscar-Nominated Movie
Creator: Kaggle
License: https://creativecommons.org/licenses/by-sa/4.0/

About this Dataset

YouTube Comments Analysis On Oscar-Nominated Movie

YouTube Comments Analysis on Oscar-Nominated Movie Trailers

Uncovering User-Generated Content

By PromptCloud [source]

About this dataset

This dataset provides an in-depth look into user-generated content for the Oscar-nominated movie trailers of the 2018 season. Created by PromptCloud, this data is a great starting point in understanding how people are engaging with these movies across YouTube. The data includes time stamps, comment text, likes and number of replies from Datasets collected till 6th March 2018. This can be used to create network graph visualizations and sentiment analysis as part of further study. PromptCloud used it’s internal web crawler to extract this filled with valuable insights about what conversations are taking place around these popular movies – which you can use to craft powerful targeted marketing campaigns!

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How to use the dataset

How to Use this Dataset

This dataset contains user-generated content from YouTube comments on Oscar-nominated movie trailers. It includes the timestamp, comment text, likes, has replies and number of replies for each comment so you can gain insight into what viewers think about the movies. Here are some ways you can use this dataset:

Analyze viewer sentiment on the nominated movies by performing sentiment analysis on the collected YouTube comments.

Vizualize relationships between commenters by creating a social network graph based on who replied to whom.

Identify trends in viewership over time by plotting out viewer engagement metrics such as likes and number of replies versus time stamp of when a comment was posted

This data was collected using PromptCloud's internal web crawler and initial study has been performed including sentiment analysis and network graph visualizations which you can view [here](https://www.promptcloud

Research Ideas

Analyzing the amount of positive/negative sentiment in comments about an Oscar-nominated movie before and after its release.

Creating a network graph to determine the relationships between commenters in response to the movie trailer and overall opinion on it.

Predicting how many likes and replies a comment will receive based on the content of its text, date posted, and other factors such as user demographics or platform used to post it

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)

You are free to:

Share - copy and redistribute the material in any medium or format for any purpose, even commercially.

Adapt - remix, transform, and build upon the material for any purpose, even commercially.

You must:

Give appropriate credit - Provide a link to the license, and indicate if changes were made.

ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: Dunkirk.csv

Column name	Description
timestamp	The time the comment was posted. (DateTime)
comment_text	The text of the comment. (String)
likes	The number of likes the comment has received. (Integer)
has_replies	Whether or not the comment has replies. (Boolean)
number_of_comments	The number of replies the comment has received. (Integer)

File: Three Billboards Outside Ebbing, Missouri.csv

Column name	Description
timestamp	The time the comment was posted. (DateTime)
comment_text	The text of the comment. (String)
likes	The number of likes the comment has received. (Integer)
has_replies	Whether or not the comment has replies. (Boolean)
number_of_replies	The number of replies the comment has received. (Integer)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit PromptCloud.

Tables

Call Me By Your Name

@kaggle.thedevastator_youtube_comments_analysis_on_oscar_nominated_mov.call_me_by_your_name

77.13 kB
980 rows
6 columns

CREATE TABLE call_me_by_your_name (
  "index" BIGINT,
  "timestamp" BIGINT,
  "comment_text" VARCHAR,
  "likes" BIGINT,
  "has_replies" BOOLEAN,
  "number_of_replies" BIGINT
);

Darkest Hour

@kaggle.thedevastator_youtube_comments_analysis_on_oscar_nominated_mov.darkest_hour

93.74 kB
913 rows
6 columns

CREATE TABLE darkest_hour (
  "index" BIGINT,
  "timestamp" BIGINT,
  "comment_text" VARCHAR,
  "likes" BIGINT,
  "has_replies" BOOLEAN,
  "number_of_replies" BIGINT
);

Dunkirk

@kaggle.thedevastator_youtube_comments_analysis_on_oscar_nominated_mov.dunkirk

1.35 MB
16,059 rows
6 columns

CREATE TABLE dunkirk (
  "index" BIGINT,
  "timestamp" BIGINT,
  "comment_text" VARCHAR,
  "likes" BIGINT,
  "has_replies" BOOLEAN,
  "number_of_comments" BIGINT
);

Get Out

@kaggle.thedevastator_youtube_comments_analysis_on_oscar_nominated_mov.get_out

816.09 kB
9,189 rows
6 columns

CREATE TABLE get_out (
  "index" BIGINT,
  "timestamp" BIGINT,
  "commenttext" VARCHAR,
  "likes" BIGINT,
  "hasreplies" BOOLEAN,
  "numberofreplies" BIGINT
);

Lady Bird

@kaggle.thedevastator_youtube_comments_analysis_on_oscar_nominated_mov.lady_bird

122.08 kB
1,779 rows
6 columns

CREATE TABLE lady_bird (
  "index" BIGINT,
  "timestamp" BIGINT,
  "comment_text" VARCHAR,
  "likes" BIGINT,
  "has_replies" BOOLEAN,
  "number_of_replies" BIGINT
);

Phantom Thread

@kaggle.thedevastator_youtube_comments_analysis_on_oscar_nominated_mov.phantom_thread

85.04 kB
1,155 rows
6 columns

CREATE TABLE phantom_thread (
  "index" BIGINT,
  "timestamp" BIGINT,
  "comment_text" VARCHAR,
  "likes" BIGINT,
  "has_replies" BOOLEAN,
  "number_of_replies" BIGINT
);

The Post

@kaggle.thedevastator_youtube_comments_analysis_on_oscar_nominated_mov.the_post

142.5 kB
1,604 rows
6 columns

CREATE TABLE the_post (
  "index" BIGINT,
  "timestamp" BIGINT,
  "comment_text" VARCHAR,
  "likes" BIGINT,
  "has_replies" BOOLEAN,
  "number_of_replies" BIGINT
);

The Shape Of Water

@kaggle.thedevastator_youtube_comments_analysis_on_oscar_nominated_mov.the_shape_of_water

364.72 kB
5,667 rows
6 columns

CREATE TABLE the_shape_of_water (
  "index" BIGINT,
  "timestamp" BIGINT,
  "comment_text" VARCHAR,
  "likes" BIGINT,
  "has_replies" BOOLEAN,
  "number_of_replies" BIGINT
);

Three Billboards Outside Ebbing Missouri

@kaggle.thedevastator_youtube_comments_analysis_on_oscar_nominated_mov.three_billboards_outside_ebbing_missouri

119.65 kB
1,501 rows
6 columns

CREATE TABLE three_billboards_outside_ebbing_missouri (
  "index" BIGINT,
  "timestamp" BIGINT,
  "comment_text" VARCHAR,
  "likes" BIGINT,
  "has_replies" BOOLEAN,
  "number_of_replies" BIGINT
);