FunTube: Annotating Funniness in YouTube Comments
نویسندگان
چکیده
Sentiment analysis has become a popular and challenging area of research in computational linguistics (e.g., [3, 6]) and even digital humanities (e.g., [10]), encompassing a range of research activities. Sentiment is often more complicated than a positive/neutral/negative distinction, dealing with a wider range of emotions (cf. [2]), and it can be applied to a range of types of text, e.g., on YouTube comments [9]. Sentiment is but one aspect of meaning, however, and in some situations it can be difficult to speak of sentiment without referencing other semantic properties. We focus on developing an annotation scheme for YouTube comments, tying together comment relevance, sentiment, and, in our case, humor. Our overall project goal is to develop techniques to automatically determine which of two videos is deemed funnier by the collective users of YouTube. There is work on automatically categorizing YouTube videos on the basis of their comments [5] and on automatically analyzing humor [4]. Our setting is novel in that for YouTube comments each comment does not necessarily itself contain anything humorous, but rather points to the humor within another source, namely its associated video (bearing some commonality with text commentary analysis, e.g., [11]). For our annotation of user comments on YouTube humor videos, a standard binary (+/-) funny annotation would ignore many complexities, stemming from different user motivations to leave comments, none of which include explicitly answering our question. We often find comments such as Thumbs up for Reginald D. Hunter! (https://www.youtube.com/watch?v=tAwZL3n9kGE), which is clearly positive, but it is unclear whether it is about funniness. We have developed a multi-level annotation scheme (section 3) for a range of video types (section 2) and have annotated user comments for a pilot set of videos. We have also investigated the impact of annotator differences on automatic classification (section 5). A second contribution of this work, then, is to investigate the connection between annotator variation and machine learning outcomes, an important step in the annotation cycle [8] and in comparing annotation schemes.
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تاریخ انتشار 2017