Difference between revisions of "Project Second Draft-Subhodeep Manaj"
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== Goal of the Project == | == Goal of the Project == | ||
| + | Youtube has a large user-base (nearly 48.2 m users in early 2010*) that are involved in discussion by posting comments on the videos they watch. People appreciate, condemn or sometimes just neutrally discuss the content of the video. Along with comments posted to a video, users also exhibit their preferences by the “liking” or “disliking” a video. | ||
| + | |||
| + | Our goal is to be able to an we predict, through the comments, whatportion of users tend to like or dislike the video. We use the actual “like” and “dislike” figures to evaluate the prediction, given the absence of labeled comments. There’s a positive correlation between the number of comments and number of ratings (likes/dislikes) for a particular video. From large sample approximations, we can assume that the number of people “liking” a video and/or commenting fairly about is an accurate representation of user's preference for that video. The same holds for “disliking” the video or commenting negatively about it. | ||
| + | |||
| + | While this seems loilke | ||
| + | • How large is large enough?.....We can take top 50 or | ||
| + | so “most discussed” videos in a category, and take the | ||
| + | ones that have high #ratings/#comments ratio | ||
| + | • To ensure getting good bias, choose the videos with | ||
| + | high variance in #likes and #dislikes, and consider | ||
| + | categories like politics, sports and music | ||
| + | |||
| + | Making use of internet slangs* | ||
| + | * http://www.internetslang.com/ | ||
| + | |||
| + | (Other)Methodology | ||
| + | |||
| + | • Making use of adjectives (and SentiWordNet) | ||
| + | • Making use of certain polar words# | ||
| + | • When comments are long and use words of both | ||
| + | polarities, collocation of certain “keywords” with | ||
| + | the polar terms can “possibly” be considered | ||
| + | • These keywords could come from a frequency | ||
| + | count over all the comments, and also from tags of | ||
| + | the video | ||
| + | • Do Latent Semantic Analysis on Comment set | ||
| + | • # Analogous to Pang et. al. | ||
| − | |||
Revision as of 18:14, 15 February 2011
Contents
Project Proposal
Predicting proportion of users that like a Youtube video through the comments on the blog
Team Members
Subhodeep Moitra (smoitra@cs.cmu.edu) Manaj Srivastava (manajs@cs.cmu.edu)
Goal of the Project
Youtube has a large user-base (nearly 48.2 m users in early 2010*) that are involved in discussion by posting comments on the videos they watch. People appreciate, condemn or sometimes just neutrally discuss the content of the video. Along with comments posted to a video, users also exhibit their preferences by the “liking” or “disliking” a video.
Our goal is to be able to an we predict, through the comments, whatportion of users tend to like or dislike the video. We use the actual “like” and “dislike” figures to evaluate the prediction, given the absence of labeled comments. There’s a positive correlation between the number of comments and number of ratings (likes/dislikes) for a particular video. From large sample approximations, we can assume that the number of people “liking” a video and/or commenting fairly about is an accurate representation of user's preference for that video. The same holds for “disliking” the video or commenting negatively about it.
While this seems loilke • How large is large enough?.....We can take top 50 or so “most discussed” videos in a category, and take the ones that have high #ratings/#comments ratio • To ensure getting good bias, choose the videos with high variance in #likes and #dislikes, and consider categories like politics, sports and music
Making use of internet slangs*
(Other)Methodology
• Making use of adjectives (and SentiWordNet) • Making use of certain polar words# • When comments are long and use words of both polarities, collocation of certain “keywords” with the polar terms can “possibly” be considered • These keywords could come from a frequency count over all the comments, and also from tags of the video • Do Latent Semantic Analysis on Comment set • # Analogous to Pang et. al.
Data Set
We will scrape youtube using an API so as to extract comments and other metadata such as number of likes, related video titles and number of views for a predefined genre of videos such as "music videos"
Evaluation Metric
Our evaluation metric will be the number of likes and dislikes for a particular video.
Filtering junk comments
An important part of our approach will be preprocessing the set of comments so as to filter out comments that are not relevant to the topic. A number of users also post spam comments such as links to their websites. We plan to incorporate a model that can classify comments as spam and reject them.
References
Stefan Siersdorfer, Jose San Pedro, Sergiu Chelaru, Wolfgang Nejdl "How useful are your comments?- Analyzing and Predicting YouTube Comments and Comment Ratings " - 19th International World Wide Web Conference, WWW 2010, Raleigh, USA
Hu M., Sun A., Lim E., “Comments-Oriented Blog Summarization by Sentence Extraction”, 16th ACM Conference on Information and Knowledge Management, 2007
Mishne G., Glance N., “Leave a Reply: An Analysis of Weblog Comments”, Third Annual Workshop on the Web-logging Ecosystem, 2006
Schuth A., Marx M., Rijke M., “Extracting the discussion structure in comments on news-articles”, Proceedings of the 9th Annual ACM Workshop on Web-Information and Data Management, 2007
