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Tv show dataset

By | 04.10.2020

Documentation Schemas Home. Example 1. Example 2. An actor, e. Actors can be associated with individual items or with a series, episode, clip. Supersedes actors. A season that is part of the media series. Supersedes season. The country of the principal offices of the production company or individual responsible for the movie or program. A director of e. Directors can be associated with individual items or with a series, episode, clip.

Supersedes directors. An episode of a tv, radio or game media within a series or season. Supersedes episodes. The production company or studio responsible for the item e. The end date and time of the item in ISO date format. The start date and time of the item in ISO date format. The subject matter of the content.

Inverse property: subjectOf. An abstract is a short description that summarizes a CreativeWork. The human sensory perceptual system or cognitive faculty through which a person may process or perceive information. A list of single or combined accessModes that are sufficient to understand all the intellectual content of a resource. Expected values include: auditory, tactile, textual, visual. Indicates that the resource is compatible with the referenced accessibility API WebSchemas wiki lists possible values.

Identifies input methods that are sufficient to fully control the described resource WebSchemas wiki lists possible values.And the currency of modern business is often represented by data. Of the data that allows the movie and TV industries to function, very little is open. Some even have a proper open data license.

tv show dataset

Good Films is a data service and a social network based on a movie recommendation engine. This service aims at facilitating the discovery of movies worth watching by searching the specific subsets of movies available on specific services like Netflix and iTunes.

Movielens is a closed source movie recommendation engine based on current data. The aim of the this site is to provide you with movies you will want to watch. The site does not provide links to platforms where you can actually enjoy the newfound movies or buy tickets.

Box Office Mojo is the site where to look up movie performance since the dawn of ages. It features closed source box office data, a movie star bank-ability index, analysis of the theatrical market but also Home Video data. This website aggregates a ton of business data around movie releases including both US, China, France, Germany and Italy. Cornell Movie-Dialogs Corpus is a large metadata-rich collection of fictional conversations extracted from raw movie scripts.

Historical and not updated. Scripts from all Seinfeld episodes. And if you liked what you read here, please subscribe to my almost monthly newsletter where I tackle marketing, productivity, entertainment and innovation. Get a personal, heartfelt update from me, with insights on marketing, presentations, productivity and life goals. To make a change happen. Have you ever sold anything while presenting? I'm serious. You are on stage, the spotlight is on you, you are talking. Can your client buy This article is an exercise in openness.

It's not merely a way for me to show off what I achieved in And you're not supposed to Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. Submit Comment. Necessary cookies are absolutely essential for the website to function properly.

This category only includes cookies that ensures basic functionalities and security features of the website.It's about the day to day life at a paper company in Scranton, Pennsylvania.

tv show dataset

Even though the show has an ensemble cast, the four characters that appear more than anyone else are: 1. Michael Scott, the regional manager 2. Dwight Shrute, paper salesman and self-appointed second-in-command 3. Jim Halpert, rival paper salesman, and 4. Pam Beesly, receptionist.

The dataset for this project is the text for all spoken lines for the entire run of the show. Each line of the data contains: seasonepisodescenespeaker, and the line text. The Office was without question an overwhelming success. So what made it so good? Why do people like it? These are the imdb viewer ratings for each episode of the office rated on a scale of 1 to The highest up-spike is the series finale.

Another one occurs in season 7 when Michael leaves the show. There's also an episode in season 5 that followed the Super Bowl that was well-received.

The most unpopular episode was in season 6, which can be explained by the fact that it was a "clip" episode, meaning that it consisted primarily of scenes cut from previous episodes. These are external explanations and other important factors include the stellar cast and directing, but what can we do with what we have?

Can we find any clues to the show's success within the script itself? For me, this is done as a fan of the show, entirely out of curiosity, but I can imagine that a writer or TV exec. Another audience that might find these answers useful are people in the digital humanities.

From the lineshare breakdown for each of the nine seasons, we can see that Michael clearly speaks the most by far until he leaves the show in season 7. The next three most common speakers are Dwight, Jim, and Pam. Fifth place rotates for a while but eventually settles on Andy. This is an attempt at measuring the relationships in the show. People are social creatures, and they care about how they relate with each other.

These relationships can be visualized a few different ways. If you take the characters by pairs, you can build a heatmap where the heat is their co-occurrence frequency. Alternatively, if you think as characters as nodes, you can use co-occurrence to draw edges between them and create a social graph.

Below you can see the centrality for the characters during season 4. Instead of Michael dominating, as in lineshare, the top spots for centrality are more evenly divided among the central characters. Using these tools, we can inspect popular or less popular episodes to see if anything stands out. Essentially this has been an exercise in feature engineering.

TV from a Data Perspective - An Analysis of NBC's The Office

We've taken the dataset and come up with ways to quantify three different aspects of it. Even though they are reasonable attempts grown from intuition, how accurately these metrics describe reality and how useful they actually are remains to be seen and depends on the tasks they are ultimately used for.

The next step would be to fulfill the initial promise and try and correlate these measures to episode rating. Another might be to apply these measures across different TV shows and see if they are useful in differentiating them. Other avenues to look for new features include using natural language processing e.Subsets of IMDb data are available for access to customers for personal and non-commercial use.

You can hold local copies of this data, and it is subject to our terms and conditions. The data is refreshed daily. The first line in each file contains headers that describe what is in each column. The available datasets are as follows: title. One or more of the following: "alternative", "dvd", "festival", "tv", "video", "working", "original", "imdbDisplay".

New values may be added in the future without warning attributes array - Additional terms to describe this alternative title, not enumerated isOriginalTitle boolean — 0: not original title; 1: original title title. Fields include: tconst string - alphanumeric unique identifier of the title directors array of nconsts - director s of the given title writers array of nconsts — writer s of the given title title.

Fields include: tconst string - alphanumeric identifier of episode parentTconst string - alphanumeric identifier of the parent TV Series seasonNumber integer — season number the episode belongs to episodeNumber integer — episode number of the tconst in the TV series title.

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Open Data Entertainment: the Best Data Sets Related to Cinema and TV

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It was still better than Life of Pi which, aside from being irrational, included no estimations of Pi at all. Miklason March 13, 2014Format: PaperbackThe plot was confusing. Was it 7899365 or 522994. The author doesn't explain 4836255's involvement and what the hell was up with 908872. I will admit that 912243 made me cry, and I nearly busted a gut over 3345221.

My emotions were just all over the place. ByTJ Holmeson March 3, 2014Format: PaperbackDid Rand's marketing department make a mistake. That means each digit only costs 0.

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tv show dataset

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10 Amazing TV Shows You Wish You Knew Earlier!

We know that consumers look to friends, family, and even strangers for feedback and recommendations on services and products. They trust the word of mouth marketing.What has actually happened Mortgage rates, and other interest rates, have fallen in response to Brexit. Analysis The former prime minister and chancellor presumably expected sterling to fall far more than it did after the vote, forcing the Bank of England to raise rates to attract money back to Britain.

But although the pound has fallen, the decline has not been calamitous (see below). As a result, the Bank was able to cut rates earlier this month. Those who want to take out fixed-rate loans have also seen rates fall. The price of these deals is driven by interest rates in the broader financial markets, such as yields on gilts (government bonds), which have fallen to new record lows.

What has actually happened The pound has fallen and inflation is expected to rise, although the effects take time to work through the economy. Analysis The fall in sterling predicted before the vote has come to pass, and inflation can be expected to follow in time.

What has actually happened Theresa May has committed to keeping the triple lock for the rest of this parliament. Economic developments could render the triple lock unaffordable for the next government. Baroness Altmann, who was pensions minister under Mr Cameron, has said the commitment to the 2. What has actually happened After an initial sharp fall, the stock market has rallied strongly to levels higher than those before the vote.

Analysis Most workers other than fortunate members of final salary pension schemes have their retirement savings tied up in the stock market. Share prices therefore have a direct impact on the financial prospects of millions of Britons.

The London stock market did fall dramatically in the days after the Brexit vote, but has recovered strongly since. However, younger pension savers would arguably be better off with lower share prices, which would allow their regular contributions to buy more shares or fund units. Their older counterparts will be more concerned with the value of their accumulated pots, which will broadly be higher in the wake of the vote.

First, some property funds were forced to suspend trading following a rush of investors who wanted to take out their money.

Many of these funds remain suspended. Second, final salary pension schemes have suffered from the further decline in gilt yields and interest rates.

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