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Tracks & Trails

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Theory Of Everything(2014)213 Available Subtitles BETTER

In June 2011, the National Association of the Deaf represented by the Disability Rights Education and Defense Fund (DREDF) filed a lawsuit against Netflix under the Americans with Disabilities Act over a lack of subtitles.[167] In October 2012, the parties agreed to a settlement under which Netflix agreed to pay $755,000 in legal fees, provide closed captioning for its entire library by 2014, and have captions available for all new content within 7 days by 2016.[168] In April 2015, the United States Court of Appeals for the Ninth Circuit issued an unpublished decision ruling that the ADA did not apply to Netflix in this case, as it is "not connected to any actual, physical place" and thus not a "place of public accommodation" that applies to the Act.[169]

Theory of Everything(2014)213 Available subtitles

The proliferation of streaming channels and international satellite has been described as an opportunity to made available famous series and films in less-used or minoritized languages, and compensate the subtitles or dubbing costs by broadcasting them to larger audiences.[181][182] In parallel, on-demand streaming (including Netflix) has become increasingly popular in children and teenagers' audiovisual preferences, which implies the use of majority languages in their home, interpersonal and leisure relationships.[183] Despite the reported efforts by Netflix to include human diversity (mostly non-white),[184] since the 2020s several studies, organizations and social movements mostly from Europe have protested for the lack of inclusion of language diversity in both the browsing interface, the searching algorithms and the content catalogues of Netflix.[185][186][187][188]

According to our recent review, in 2022, in addition to our own 27,000-sentence data set based on social media, the only publicly available Finnish language data sets with manual sentiment annotations are the 6427 sentences published by Kajava (2018) and the 25,000 sentences by Öhman et al. (2020) based on movie subtitles.

In our survey of previous work, we noted that there were only two data sets for sentiment analysis of movie subtitles available for Finnish but no large-scale social media data set with sentiment polarity annotations. This publication remedies this shortcoming by introducing a 27,000-sentence data set annotated independently with sentiment polarity by three native annotators. The same three annotators annotated the whole data set. This is in contrast to other data sets, which have usually been annotated piecemeal by many annotators. Being university students, the annotators belong to a similar demographic, which might introduce some bias. However, bias detection is a research topic of its own and our resource with consistent annotations by one demographic is a valuable starting point for such research.

Subtitles are available in a SubRip Text (SRT) format and consist of four basic information (Fig. 4): (1) a number to identify the order of the subtitles; (2) the beginning and ending time (hours, minutes, seconds, milliseconds) in which the subtitle should appear in the movie; (3) the subtitle text itself on one or more lines and (4) typically an empty line to indicate the end of the subtitle block. However, subtitles do not include information about characters, scenes, shots, and actions whereas dialogues in a script do not include time information.

In this paper, we introduce a multilayer model with movie elements characters, locations, keywords, faces and captions are in interaction. Unlike single layer networks which usually focus only on characters or scenes, this model is much more informative. It completes the single character network analysis with a new topological analysis made of more semantic elements that brings us a global broad picture of the movie story. We also propose an automatic method to extract the multilayer network elements from the script, subtitles, and movie content. In order to enrich the previous model, additional multimedia elements are included, such as face recognition, dense captioning and subtitle information. We have publicly released all our multilayer network datasets and made them available at 041b061a72


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