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Know Your Data: Understanding Implicit Usage versus Explicit Action in Video Content Classification

机译:了解您的数据:了解视频内容分类中的隐式用法与显式操作

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In this paper, we present a method for video category classification using only social metadata from websites like YouTube. In place of content analysis, we utilize communicative and social contexts surrounding videos as a means to determine a categorical genre, e.g. Comedy, Music. We hypothesize that video clips belonging to different genre categories would have distinct signatures and patterns that are reflected in their collected metadata. In particular, we define and describe social metadata as usage or action to aid in classification. We trained a Naive Bayes classifier to predict categories from a sample of 1,740 YouTube videos representing the top five genre categories. Using just a small number of the available metadata features, we compare the classifications produced by our Naive Bayes classifier with those provided by the uploader of that particular video. Compared to random predictions with the YouTube data (21% accurate), our classifier attained a mediocre 33% accuracy in predicting video genres. However, we found that the accuracy of our classifier significantly improves by nominal factoring of the explicit data features. By factoring the ratings of the videos in the dataset, the classifier was able to accurately predict the genres of 75% of the videos. We argue that the patterns of social activity found in the metadata are not just meaningful in their own right, but are indicative of the meaning of the shared video content. The results presented by this project represents a first step in investigating the potential meaning and significance of social metadata and its relation to the media experience.
机译:在本文中,我们提出了一种仅使用来自YouTube等网站的社交元数据进行视频类别分类的方法。代替内容分析,我们利用视频周围的交流和社交环境来确定分类体裁,例如喜剧,音乐。我们假设属于不同流派类别的视频剪辑将具有不同的签名和模式,这些签名和模式会反映在其收集的元数据中。特别是,我们将社交元数据定义和描述为有助于分类的用法或动作。我们训练了一个朴素的贝叶斯分类器,以从代表前五个流派类别的1,740个YouTube视频样本中预测类别。仅使用少量可用的元数据功能,我们将朴素贝叶斯分类器产生的分类与该特定视频的上传者提供的分类进行比较。与使用YouTube数据进行的随机预测(准确度为21%)相比,我们的分类器在预测视频类型方面的准确度仅为33%。但是,我们发现分类器的准确性通过显式数据特征的标称因子显着提高。通过将数据在视频数据集中的分级考虑在内,分类器能够准确预测75%的视频流派。我们认为,元数据中发现的社交活动模式不仅本身具有意义,而且还指示了共享视频内容的含义。该项目提供的结果代表了调查社会元数据的潜在含义和意义及其与媒体体验的关系的第一步。

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