ELIS – Multimedia Lab Towards Twitter Hashtag Recommendation Using Distributed Word Representations and a Deep Feed Forward Neural Network CSSC-2014 New Delhi, 24 September 2014 Abhineshwar Tomar, Frederic Godin, Baptist Vandersmissen, Wesley De Neve, Rik Van de Walle Multimedia Lab, Ghent University – iMinds, Belgium Image and Video Systems Lab, KAIST, South Korea
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Towards Twitter hashtag recommendation using distributed word representations and a deep feed forward neural network
Towards Twitter hashtag recommendation using distributed word representations and a deep feed forward neural network
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ELIS – Multimedia Lab
Towards Twitter Hashtag Recommendation Using Distributed Word Representations and a Deep Feed
Forward Neural Network
CSSC-2014New Delhi, 24 September 2014
Abhineshwar Tomar, Frederic Godin, Baptist Vandersmissen, Wesley De Neve, Rik Van de Walle
Multimedia Lab, Ghent University – iMinds, BelgiumImage and Video Systems Lab, KAIST, South Korea
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ELIS – Multimedia Lab
Introduction Goal Motivation Methodology Results Conclusion Future work
Overview
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ELIS – Multimedia Lab
Introduction Goal Motivation Methodology Results Conclusion Future work
Overview
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ELIS – Multimedia Lab
• An online social network service that enables users to send and read short 140-character text messages, called "tweets" or "microposts"
Twitter
Tweet ormicropostRetweet
(sharing)
Favorite(like or
bookmark)
Mention(starts with @)
Hashtag(starts with #)
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ELIS – Multimedia Lab
Note the presence of both textual and (embedded) visual information!
Famous Tweets
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ELIS – Multimedia Lab
• Usage in general- 271 million monthly active users- 500 million Tweets are sent per day
• Hashtags- Only 8% of the tweets contain hashtags- 3% of the hashtags are used more than 5 times
Twitter Statistics
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ELIS – Multimedia Lab
Hashtags on Twitter
Hashtag usage:- topic-based indexing & search
• #socialnetwork• #Reddit
- conversational/event clustering• #www2014
Observation: only 8% of tweets contain a hashtag
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ELIS – Multimedia Lab
Introduction Goal Why Methodology Results Conclusion Future work
Overview
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ELIS – Multimedia Lab
Generate hashtags that adhere to the semantic and linguistic regularity of a tweet
Goal
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ELIS – Multimedia Lab
Introduction Goal Motivation Methodology Results Conclusion Future work
Overview
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ELIS – Multimedia Lab
• Hashtags- Content categorization and discovery- Effective search of tweets
• Our approach- Connect similar hashtags (topics)- Promote the use of hashtags
• By understanding the semantics of the tweet
Why
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ELIS – Multimedia Lab
Introduction Goal Motivation Methodology Results Conclusion Future work