爛番茄

Multi tool usebody.skin-minerva .mw-parser-output table.infobox captiontext-align:center
爛番茄
Rotten Tomatoes
 |
网站类型 | 線上電影和電子遊戲的評論 |
---|
持有者 | NBC環球(康卡斯特) 華納兄弟(時代華納) |
---|
创始人 | Senh Duong |
---|
网站 | http://www.rottentomatoes.com/
|
---|
商业性质 | 是 |
---|
注册 | 可選擇 |
---|
推出时间 | 1998年8月12日 |
---|
爛番茄(英文:Rotten Tomatoes)是一個網站,以提供電影、電子遊戲及电视节目的相關評論、資訊和新聞為主。1998年8月,网站由加州大学伯克利分校(UC Berkeley)的三位亚裔本科生创建:Senh Duong, Patrick Y. Lee and Stephen Wang。[1][2][3][4]網站的名稱是因歌舞雜耍表演(vaudeville)時期觀眾在演出不佳時會往舞台上扔擲番茄和其他蔬果而來。
爛番茄的工作人員會搜尋各網站上刊載的特定電影或遊戲評論,不論是業餘或專業的。一旦搜尋到之後,工作人員使用整合資料(aggregate data)來決定評論是正面(「新鮮」(fresh),以一個鮮紅的番茄作為標記)或負面(「腐爛」(rotten),以一個綠色被砸爛的番茄作為標記)。
網站會追蹤所有的評論內容(一些主要的大型電影約能達到250篇)以及正面評價的比例。若正面的評論超過60%以上,該部作品將會被認為是「新鮮」(fresh)。相反的,若一部作品的正面評價低於60%,則該作品會被標示為「腐爛」(rotten)。此外,知名的影評人如羅傑·艾伯特、狄森·湯瑪森(Desson Thomson)和史帝芬·杭特(Stephen Hunter,華盛頓郵報)等人會被列在網頁上一個名為「Cream of the Crop」的專區,將他們的影評獨立呈現,同時也依然將這些知名作者的影評列入電影的整體評分計算中。
許多想看電影的民眾,都會參考這個網站,例如蘋果iTunes Store和Google Play 電影也會加以連結評價,以便民眾參考。
参考
^ How Rotten Tomatoes became Hollywood's most influential — and feared — website. Los Angeles Times. [2018-04-18]. ISSN 0458-3035 (美国英语).
^ Entrepreneurial Best Practices Series: A Fireside Chat with Rotten Tomatoes Founder Patrick Lee - Berkeley-Haas Entrepreneurship Program. Berkeley-Haas Entrepreneurship Program. [2018-04-18] (美国英语).
^ Notable Cal Alumni. Cal Alumni Association. 2018-02-21 [2018-04-18].
^ Stephen Wang. angel.co. [2018-04-18].
外部連結
RottenTomatoes.com(英文)
爛番茄的Twitter帳戶(英文)
爛番茄的Facebook專頁(英文)
YouTube上的爛番茄頻道(英文)
爛番茄的Tumblr部落格(英文)
爛番茄在Google+的页面(英文)
Tde3Nog,RkFUNxhIPzMCd7WFU0e aBf
Popular posts from this blog
Ramiro Burr's New Blog - to go back: www.ramiroburr.com From Latin rock to reggaeton, boleros to blues,Tex-Mex to Tejano, conjunto to corridos and beyond, Ramiro Burr has it covered. If you have a new CD release, a trivia question or are looking for tour info, post a message here or e-mail Ramiro directly at: musicreporter@gmail.com Top Tejano songwriter Luis Silva dead of heart attack at 64 By Ramiro Burr on October 23, 2008 8:40 AM | Permalink | Comments (12) | TrackBacks (0) UPDATE: Luis Silva Funeral Service details released Visitation 4-9 p.m. Saturday, Rosary service 6 p.m. Saturday at Porter Loring, 1101 McCullough Ave Funeral Service 10:30 a.m. Monday St. Anthony De Padua Catholic Church, Burial Service at Chapel Hills, 7735 Gibbs Sprawl Road. Porter Loring (210) 227-8221 Related New Flash: Irma Laura Lopez: long time record promoter killed in accident NewsFlash: 9:02 a.m. (New comments below) Luis Silva , one of the most well-known ...
1 I having trouble getting my ResourceDictionary.MergedDictionaries to load from app.xaml. My WPF app has a static class with a Main defined and startup object set to it. Within Main I created an instance of App and run it. The override OnStartup fires and the mainwindow.cs InitializeComponent gives the error "Message "Cannot find resource named 'MaterialDesignFloatingActionMiniAccentButton'. If I put the resources in the mainwindow.xaml everything is fine, but I wanted them to load at the app level so I they are not in each page. Any help appreciated. public partial class App protected override void OnStartup(StartupEventArgs e) base.OnStartup(e); var app = new MainWindow(); var context = new MainWindowViewModel(); app.DataContext = context; app.Show(); from the Main.. var app = new App(); app.Run(); app.xaml.. <Application x:Class="GS.Server.App" xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" xmlns:...
up vote 2 down vote favorite There is a clear pattern that show for two separate subsets (set of columns); If one value is missing in a column, values of other columns in the same subset are missing for any row. Here is a visualization of missing data My tries up until now, I used ycimpute library to learn from other values, and applied Iterforest. I noted, score of Logistic regression is so weak (0.6) and thought Iterforest might not learn enough or anyway, except from outer subset which might not be enough? for example the subset with 11 columns might learn from the other columns but not from within it's members, and the same goes for the subset with four columns. This bar plot show better quantity of missings So of course, dealing with missings is better than dropping rows because It would affect my prediction which does contain the same missings quantity relatively. Any better way to deal with these ? [EDIT] The nullity pattern is confirmed: machine-learning cor...