Reviews of IMDB Movies
Exploring Ratings, Genres, and Spoilers
By [source]
About this dataset
This dataset houses user reviews and ratings of movies from the popular Internet Movie Database (IMDB). Our IMDB movie reviews data contains detailed sentiment analysis from users on thousands of films. With the help of this dataset, we can explore the opinions and attitudes of viewers about a wide range of titles. The columns include information such as a user's username, date posted, helpfulness ratings, spoiler alert level, genre classification and review title. By utilizing this data to its fullest potential we can learn more about why people are drawn to certain types of films and which movies may have been overlooked by general audiences. In doing so we can gain a better understanding for how preferences for different genres have changed over time as well as discover hidden gems that should not be missed!
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How to use the dataset
This dataset contains user reviews for movies in the IMDB database. It includes several columns of data such as username, date, review title, rating, text, helpfulYes (the number of users who found the review helpful), helpfulTotal (the total number of users who voted on the review), isSpoiler (whether or not the review contains spoilers), and genre.
To use this dataset effectively to gain insights into IMDB movie ratings and reviews from actual viewers:
- Start by exploring user ratings over time to identify any noticeable trends that could be used to develop marketing strategies or inform programming decisions. For example, see which genres consistently receive higher or lower ratings over time in order to better target audiences.
- Analyze how specific words within reviews are rated differently across genres or languages; word frequency can be seen as a measure of reviewer sentiment toward each film's content - look for patterns between amount of positive/negative words used in different language versions/$genres etc).
- Utilize helpfulness scores by looking at how many people are engaging with each individual review - see where other reviewers find value within a given user's commentaries and identify which ones stand out from all the others! Finally analyze spoiler access within comments too- determine whether viewers find warning labels actually effective at deterring them away..
Research Ideas
- Identifying correlation between specific review characteristics (ie. length, rating, use of keywords) and helpfulness ratings to find patterns in user reviews and optimize the usefulness of future reviews
- Analyzing user preferences for certain genres or ratings to help marketers modify movies based on user desires
- Utilizing Natural Language Processing on the text data to identify what users generally like/dislike about movies in order to better personalize movie recommendations for viewers
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: reviews.csv
| Column name |
Description |
| username |
The username of the reviewer. (String) |
| date |
The date the review was posted. (Date) |
| review_title |
The title of the review. (String) |
| rating |
The rating given to the movie by the reviewer. (Integer) |
| text |
The text of the review. (String) |
| helpfulYes |
The number of users who found the review helpful. (Integer) |
| helpfulTotal |
The total number of users who voted on the helpfulness of the review. (Integer) |
| isSpoiler |
Whether or not the review contains spoilers. (Boolean) |
File: genres.csv
| Column name |
Description |
| genre |
The genre of the movie. (String) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .