Learning to Rank for Joy

Date

2014-04-11

Author

Orellana-Rodriguez, Claudia
Nejdl, Wolfgang
Diaz-Aviles, Ernesto
Altıngövde, İsmail Sengör

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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User-generated content is a growing source of valuable information and its analysis can lead to a better understanding of the users needs and trends. In this paper, we leverage user feedback about YouTube videos for the task of affective video ranking. To this end, we follow a learning to rank approach, which allows us to compare the performance of different sets of features when the ranking task goes beyond mere relevance and requires an affective understanding of the videos. Our results show that, while basic video features, such as title and tags, lead to effective rankings in an affective-less setup, they do not perform as good when dealing with an affective ranking task.

Subject Keywords

Sentiment analysis, Social media analytics, YouTube

URI

https://hdl.handle.net/11511/42250

DOI

https://doi.org/10.1145/2567948.2576961

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

C. Orellana-Rodriguez, W. Nejdl, E. Diaz-Aviles, and İ. S. Altıngövde, “Learning to Rank for Joy,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42250.