The findings of a survey held in the United States in September 2021 revealed that ** percent of adults aged between 35 and 44 years old said that they watched or streamed movies every day, making respondents in this age group the most likely to do so. By comparison, ** percent of total respondents reported watching movies on a daily basis.

According to a study held in March 2020, ** percent of adults in the United States are much more or somewhat more likely to watch movies via a streaming service due to the coronavirus outbreak. Over ** percent of adults also said that they were likely to watch more televison online, and many consumers also stated that they would probably sign up to a new service as a result of the pandemic. Millennial and Gen Z consumers favored movies over TV shows, and Boomers were notably less likely to increase their consumption of either streamed films or television.

Movies Performance and Feature Statistics

Analyzing Box Office Performance, Rating and Audience Reactions

By Yashwanth Sharaff [source]

About this dataset

This dataset contains essential characteristics of a variety of movies, including basic pieces of information such as the movie's title and budget, as well as performance indicators like the movie's MPAA rating, gross revenue, release date, genre, runtime, rating count and summary. With this data set we can better understand the film industry and uncover insights on how different features and performance metrics impact one another to guarantee a movie's success. The movies dataset also helps you make informed decisions about which features are key indicators in setting up a high-grossing feature film

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How to use the dataset

To get the most out of this data set you need to understand what each column in it represents. The ‘Title’ column gives you the title of the movie which can be used for further search or exploration on popular streaming services and websites that are dedicated to providing detailed information about movies. The ‘MPAA Rating’ lists any Motion Picture Association (MPAA) rating for a movie which consists of G (General Audiences), PG (Parental Guidance Suggested), PG-13 (Parents Strongly Cautioned), R (Under 17 Requires Accompanying Parent or Guardian) etc. The 'Budget' column give you an approximate idea about how much a particular production cost while the 'Gross' columns depicts its earnings if it was released in theaters while its successor 'Release Date' reveals when each film has been released or is going to release in future. The columns 'Genre', 'Runtime', and ‘Rating Count’ cover subje​cts such as what type of movie is it? Every genre will have an associated runtime limit along with rating count which refers to number people who have rated/reviewed a particular flick whether on IMDB or other streaming services as well as paper mediums like newspapers . Last but not least summary field states an overview of what we can expect from film so take this in account before watching anything especially if include children members in your family.

So go ahead - start exploring this interesting dataset today!

Research Ideas

• Creating a box office prediction model using budget, genre, release date and MPAA rating
• Using the summary data to create a sentiment analysis tool for movie reviews
• Building a recommendation engine for users based on their prior ratings and what other users with similar tastes have rated as highly

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: movies.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------------------------| | Title | The title of the movie. (String) | | MPAA Rating | The Motion Picture Association of America (MPAA) rating of the movie. (String) | | Budget | The budget of the movie in US dollars. (Integer) | | Gross | The gross revenue of the movie in US dollars. (Integer) | | Release Date | The date the movie was released. (Date) | | Genre | The genre of the movie. (String) | | Runtime | The length of the movie in minutes. (Integer) | | Rating Count | The number of ratings the movie has received. (Integer) | | Summary | A brief summary 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 Yashwanth Sharaff.

In August 2022, the shares of adults surveyed in the United States who preferred to watch movies at home and in theaters stood at ** and ** percent, respectively. Over four years earlier, in February 2018, the figures were significantly closer, at ** and ** percent. Between 2018 and 2021, the number of movie tickets sold in the U.S. and Canada decreased by more than ** percent.

This dataset includes statistics about durations between two consecutive subtitles in 5,000 top-ranked IMDB movies. The dataset can be used to understand how dialogue is used in films and to develop tools to improve the watching experience. This notebook contains the code and data that were used to create this dataset.

Dataset statistics:

• Average duration between subtitles
• Average duration between subtitles with a duration greater than 10, 30, 60, 120, and 300 seconds
• Maximum duration between subtitles
• Percentage of duration between subtitles from the runtime

Dataset use cases:

• Understanding how dialogue is used in movies, such as the average duration of a dialogue scene and how the duration of dialogue varies between different genres
• Developing tools to improve the watching experience by adjusting the playback speed of dialogue scenes
• Evaluating the effectiveness of tools like the VLC extension mentioned below

Data Analysis:

The next histogram shows the distribution of movie runtimes in minutes. The mean runtime is 99.903 minutes, the maximum runtime is 877 minutes, and the median runtime is 98.5 minutes.

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Figure 1: Histogram of the runtime in minutes

The next histogram shows the distribution of the percentage of gaps (duration between two consecutive subtitles) out of all the movie runtime. The mean percentage of gaps is 0.187, the maximum percentage of gaps is 0.033, and the median percentage of gaps is 327.586.

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Figure 2: Histogram of the percentage of gaps (duration between two consecutive subtitles) out of all the movie runtime

The next histogram shows the distribution of the total movie's subtitle duration (seconds) between two consecutive subtitles. The mean subtitle duration is 4,837.089 seconds and the median subtitle duration is 2,906.435 seconds.

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Figure 3: Histogram of the total movie's subtitle duration (seconds) between two consecutive subtitles

Example use case:

The Dynamic Adjustment of Playback Speed (DAPS), a VLC extension, can be used to save time while watching movies by increasing the playback speed between dialogues. However, it is essential to choose the appropriate settings for the extension, as increasing the playback speed can impact the overall tone and impact of the film.

The dataset of 5,000 top-ranked movie subtitle durations can be used to help users choose the appropriate settings for the DAPS extension. For example, users who are watching a fast-paced action movie may want to set a higher minimum duration between subtitles before speeding up, while users who are watching a slow-paced drama movie may want to set a lower minimum duration.

Additionally, users can use the dataset to understand how the different settings of the DAPS extension impact the overall viewing experience. For example, users can experiment with different settings to see how they affect the pacing of the movie and the overall impact of the dialogue scenes.

Conclusion

This dataset is a valuable resource for researchers and developers who are interested in understanding and improving the use of dialogue in movies or in tools for watching movies.

A survey conducted in Japan in 2023 showed that more than ***** percent of the respondents often watch movies multiple times at movie theaters. A majority of respondents stated that they have never watched a movie repeatedly at theaters.

IMDB Movie Metadata Multivariate Analysis

Exploring the Impact of Types, Ratings, and Genres on Audience Engagement

By Addi Ait-Mlouk [source]

About this dataset

This amazing IMDB movie metadata dataset has everything you need to uncover the secrets of cinema! Featuring a comprehensive set of variables that range from budget, revenue, and cast size to title types and genres - it holds the keys for unlocking lucrative insights about your favorite films. This dataset contains an array of information on over 10,000 film titles from 6 different countries (USA, UK, Germany, Canada, India and Japan). Its detailed columns include Movie ID (unique identifier for each film), Title Type (TV Series/Movie/Video), Production Budget & Revenue figures in USD along with a breakdown into Country-specific Currency Units such as EUR or GBP. Additionally, each entry features the Primary Genre Category it was classified under along with secondary Genres if applicable. Finally this collection includes text fields giving insight into plot keywords as well as Cast & Crew credits including names of Actors/Actresses in main roles plus other important personnel working on set such as Directors or Writers. Together all these features create an invaluable resource suitable for detailed analysis aiming to understand movie trends over time – How budgets compare across nations? What genres are doing better internationally all these fascinating questions addressed by this incredible dataset!

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How to use the dataset

This dataset contain 2000 films from IMDb, containing basic metadata on the films such as genre, budget and gross box office performance. It has a variety of columns which provide valuable insights when attempting to do multivariate analysis on this data. The data is divided into metacritic score (which is a measure of critics ratings), budget (in dollars), revenue (in dollars) and eight other stats related to movies

• Metacritic Score: A metric based on critical reviews ranging from 0 - 100 indicating how well the movie was reviewed by critics.
• Budget: Total budget in US dollars for each movie. This can be used to better understand why certain films are successful or not so successful when analyzing against other metrics in the dataset
• Revenue: Total revenue earned in US dollars for each movie at Box Office and outside digital downloads/streams & TV airplay etc… we could use this to calculate revenue per budget for movies as well as success rates of certain genres over others given higher budgets
• Release Date: The release date given in string format mainly used for understanding seasonality effects between various releases in different times throughout the year e.g summer vs winter releases
  5 .Runtime: Length of time that a film plays for expressed in minutes could be useful measuring watchability/ engagement potential or correlations with average ticket prices at theatre pricing timeframes(matinees vs evening shows)

6 Genres : Genres associated with a particular film e.g comedy ,drama , horror etc … Value information taken from freedb collection created by IMDB,these categories can also help further narrow down what people like and disliked about certain films or companies producing them

7 Directors : Each directors name usually one director per film which might give us some insight into directing decisions made which have an impact on box-office performance

8 Writers : Names of writers associated with a particular project same reasoning applies here that making different decisions around who write content reflects audience response

9 Actors/Actresses : Names of some key actors associate with major roles like leads/support takes who worked on stories will give more insight into why viewers preferred them OVER OTHER PROJECTS THEY MAY HAVE BEEN ASSOCIATED WITH along their career paths

10 Countries apart from USA origin countries are identified Here since many american productions try new approaches keeping an eye out may point us towards effective techniques being implemented abroad that have potential application to American markets ADR

Research Ideas

• Creating movie recommendation systems based on user preferences: This dataset can be used to uncover patterns in user movie watching habits and preferences by factoring in multiple variables such as genre, release year, director, cast, popularity/ratings etc., and recommending similar movies accordingly.
• Cost vs Profit predictive models: This dataset can be utilized to construct a p...

A survey conducted in Japan in February 2025 showed that *** percent of the respondents watched movies at movie theaters 12 times or more often within the past year. More than **** of the respondents stated that they did not watch movies at theaters at all.

In the context of a survey on the image of the famous French film festival in Cannes, published by Odoxa in May 2022, the respondents have been asked which services they used at what frequency to consume and watch movies. The survey found that more than ************** of French consumers watch movies on free channels on TV, making it the most popular movie consumption service. Second in the ranking came subscription services such as Netflix with still more than half of French viewers using it. Cinema itself however ranked in third position, reaching only a share of ** percent in this survey.

A survey revealed that more and more consumers pay to watch premium video-on-demand films that skipped the cinemas because of the coronavirus outbreak. According to a ********* survey one third said they did so, up from ** percent in *********. Due to the pandemic and the following theater closures, movie studios were forced to release their content on streaming services for the first time rather than in cinemas.

A survey conducted in Japan in 2023 showed that more than ** percent of the respondents preferred to watch movies at home over watching them at movie theaters. A smaller share stated that they prefer to watch movies at theaters.

Most of U.S. viewers to a 2022 survey stated to prefer to watch newly released movies online, with ** percent. Further ** percent of respondents admitted they would prefer to watch movie premieres at the movie theater.

In 2023, the average number of movies watched by a typical theatergoer in India was about ***. Telugu film audiences had the highest average, with *** films watched per person, followed by Tamil and Malayalam language films. The average number of films watched was significantly higher among audiences of South Indian languages compared to Hindi.

In January 2020, a survey held among adults in the United States revealed that ** percent of men watched or streamed movies on a daily basis, compared to ** percent of women. Data also showed that men were more likely to watch or stream sports shows more regularly, and daily consumption of TV shows was higher among women.

The statistic shows the results of a survey conducted by Cint on the distribution of film genres watched in movie theaters in Italy in 2017 and 2018. In 2018, ***** percent of respondents stated that they watch comedy movies when they go to the movie theater.

The statistic shows the results of a survey conducted by Cint on the distribution of film genres watched in movie theaters in the Netherlands in 2017 and 2018. In 2017, ***** percent of respondents stated that they watch action movies when they go to the movie theater.

The statistic shows the results of a survey conducted by Cint on the distribution of film genres watched in movie theaters in Poland from 2016 to 2018. In 2017, ***** percent of respondents stated that they watch comedy movies when they go to the movie theater.

The statistic shows the results of a survey conducted by Cint on the distribution of film genres watched in movie theaters in Spain in 2017 and 2018. In 2017, ***** percent of respondents stated that they watch action movies when they go to the movie theater.

The statistic shows the results of a survey conducted by Cint on the distribution of film genres watched in movie theaters in Ireland in 2017 and 2018. In 2017, ***** percent of respondents stated that they watch comedy movies when they go to the movie theater.

A survey conducted in Japan in 2023 showed that almost five percent of the respondents watch more than ** movies per year. About one-fifth of the respondents stated that they do not watch movies at all.