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ELIS – Multimedia Lab 
Towards Twitter Hashtag Recommendation Using 
Distributed Word Representations and a Deep Feed 
For...
2 
ELIS – Multimedia Lab 
 Introduction 
 Goal 
 Motivation 
 Methodology 
 Results 
 Conclusion 
 Future work 
Ove...
3 
ELIS – Multimedia Lab 
 Introduction 
 Goal 
 Motivation 
 Methodology 
 Results 
 Conclusion 
 Future work 
Ove...
4 
ELIS – Multimedia Lab 
Twitter 
• An online social network service that enables users to send and read 
short 140-chara...
5 
ELIS – Multimedia Lab 
Famous Tweets 
Note the presence of both textual and (embedded) visual information!
6 
ELIS – Multimedia Lab 
• Usage in general 
Twitter Statistics 
- 271 million monthly active users 
- 500 million Tweets...
7 
ELIS – Multimedia Lab 
Hashtags on Twitter 
Hashtag usage: 
- topic-based indexing & search 
• #socialnetwork 
• #Reddi...
8 
ELIS – Multimedia Lab 
 Introduction 
 Goal 
 Why 
 Methodology 
 Results 
 Conclusion 
 Future work 
Overview
9 
ELIS – Multimedia Lab 
Goal 
Generate hashtags that adhere to the 
semantic and linguistic regularity of a tweet
10 
ELIS – Multimedia Lab 
 Introduction 
 Goal 
 Motivation 
 Methodology 
 Results 
 Conclusion 
 Future work 
Ov...
11 
ELIS – Multimedia Lab 
• Hashtags 
Why 
- Content categorization and discovery 
- Effective search of tweets 
• Our ap...
12 
ELIS – Multimedia Lab 
 Introduction 
 Goal 
 Motivation 
 Methodology 
 Results 
 Conclusion 
 Future work 
Ov...
13 
ELIS – Multimedia Lab 
• Preprocessing 
- Remove non-English words 
- Remove non-ASCII characters 
- Remove mentions (...
14 
ELIS – Multimedia Lab 
Methodology (2/3) 
• Training: learning the relation between tweets and hashtags 
Tweet Hashtag...
15 
ELIS – Multimedia Lab 
Methodology (3/3) 
• Testing: recommending hashtags to tweets 
word2vec 
300-D 
tweet 
vector 
...
16 
ELIS – Multimedia Lab 
word2vec 
• Developed by Google Research 
• Computes vector representations for words 
- Throug...
17 
ELIS – Multimedia Lab 
 Introduction 
 Goal 
 Motivation 
 Methodology 
 Results 
 Conclusion 
 Future work 
Ov...
18 
ELIS – Multimedia Lab 
Results (1/3) 
Tweet Recommended hashtags 
1 Someone dm/text me bc I’m so bored madd, Oh noes, ...
19 
ELIS – Multimedia Lab 
Results (2/3)
20 
ELIS – Multimedia Lab 
Results (3/3) 
Top-k recommendation Hit-rate 
She et al. Our approach 
1 Top-5 82% 83.33% 
2 To...
21 
ELIS – Multimedia Lab 
 Introduction 
 Goal 
 Motivation 
 Methodology 
 Results 
 Conclusion 
 Future work 
Ov...
22 
ELIS – Multimedia Lab 
Conclusion 
• Introduced a novel approach for hashtag recommendation, using 
distributed word r...
23 
ELIS – Multimedia Lab 
 Introduction 
 Goal 
 Motivation 
 Methodology 
 Results 
 Conclusion 
 Future work 
Ov...
24 
ELIS – Multimedia Lab 
Future Work 
• Use of more than four days of data 
• Use word representations from different da...
ELIS – Multimedia Lab
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Towards Twitter hashtag recommendation using distributed word representations and a deep feed forward neural network

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Towards Twitter hashtag recommendation using distributed word representations and a deep feed forward neural network

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Towards Twitter hashtag recommendation using distributed word representations and a deep feed forward neural network

  1. 1. 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
  2. 2. 2 ELIS – Multimedia Lab  Introduction  Goal  Motivation  Methodology  Results  Conclusion  Future work Overview
  3. 3. 3 ELIS – Multimedia Lab  Introduction  Goal  Motivation  Methodology  Results  Conclusion  Future work Overview
  4. 4. 4 ELIS – Multimedia Lab Twitter • An online social network service that enables users to send and read short 140-character text messages, called "tweets" or "microposts" Hashtag (starts with #) Tweet or Mention (starts with @) Favorite (like or bookmark) Retweet micropost (sharing)
  5. 5. 5 ELIS – Multimedia Lab Famous Tweets Note the presence of both textual and (embedded) visual information!
  6. 6. 6 ELIS – Multimedia Lab • Usage in general Twitter Statistics - 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
  7. 7. 7 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
  8. 8. 8 ELIS – Multimedia Lab  Introduction  Goal  Why  Methodology  Results  Conclusion  Future work Overview
  9. 9. 9 ELIS – Multimedia Lab Goal Generate hashtags that adhere to the semantic and linguistic regularity of a tweet
  10. 10. 10 ELIS – Multimedia Lab  Introduction  Goal  Motivation  Methodology  Results  Conclusion  Future work Overview
  11. 11. 11 ELIS – Multimedia Lab • Hashtags Why - 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
  12. 12. 12 ELIS – Multimedia Lab  Introduction  Goal  Motivation  Methodology  Results  Conclusion  Future work Overview
  13. 13. 13 ELIS – Multimedia Lab • Preprocessing - Remove non-English words - Remove non-ASCII characters - Remove mentions (@USER) - Remove URLs - Remove RT @from retweets • Feature vector generation • Training of a feed forward neural network • Evaluation Methodology (1/3)
  14. 14. 14 ELIS – Multimedia Lab Methodology (2/3) • Training: learning the relation between tweets and hashtags Tweet Hashtag word2vec 300-D tweet vector word2vec 300-D hashtag vector Deep feed-forward neural network 300-D input layer 1000-D hidden layer 500-D hidden layer 400-D hidden layer 300-D output layer Elizabeth Warren Taking on Hillary as New Democratic Powerhouse #politics
  15. 15. 15 ELIS – Multimedia Lab Methodology (3/3) • Testing: recommending hashtags to tweets word2vec 300-D tweet vector 300-D hashtag vector Deep feed-forward neural network 300-D input layer 1000-D hidden layer 500-D hidden layer 400-D hidden layer 300-D output layer Tweet House Democrats suggest Obama impeachment is imminent to raise cash vec2word Hashtag Hashtag Hashtag Hashtags #politics #crisis
  16. 16. 16 ELIS – Multimedia Lab word2vec • Developed by Google Research • Computes vector representations for words - Through the use of neural network technology • Trained on part of the Google News dataset (+/- 100 billion words) • The model contains vectors for 3 million words and phrases - Capture the semantic meaning of a word • Example word vector properties - vector('Paris') - vector('France') + vector('Italy') ≈ vector('Rome') - vector('king') - vector('man') + vector('woman') ≈ vector('queen')
  17. 17. 17 ELIS – Multimedia Lab  Introduction  Goal  Motivation  Methodology  Results  Conclusion  Future work Overview
  18. 18. 18 ELIS – Multimedia Lab Results (1/3) Tweet Recommended hashtags 1 Someone dm/text me bc I’m so bored madd, Oh noes, rainnwilson, sooooooo, fricken 2 The good life is one inspired by love and guided by knowledge. Ahh yes, FIVE THINGS About, YANKEES TALK, Kinder gentler, Ya gotta love 3 Method of Losing Weight http://t.co/rs64CEuo5W Shape Shifting, Treat Acne, Detect Cancer, Warps, Calorie Burn 4 I hate today cause its room cleaning day for me!!! FAN ’S ATTIC, Puh leez, Mopping robot, % #F######## 3v.jsn, Interest EURO JAP 5 SPELLS AND SPELL-CASTING:ENCYCLOPEDIA OF 5000 SPELLS ( JUDIKA ILLES ):BLACKSMITH’S WATER HEALING SPELL: A... http://t.co/k0TfrqJFQW DEBUTS NEW, NOW AVAILABLE FOR, TO PUBLISH, DESIGNED TO, IS READY TO
  19. 19. 19 ELIS – Multimedia Lab Results (2/3)
  20. 20. 20 ELIS – Multimedia Lab Results (3/3) Top-k recommendation Hit-rate She et al. Our approach 1 Top-5 82% 83.33% 2 Top-10 89% 86.67%
  21. 21. 21 ELIS – Multimedia Lab  Introduction  Goal  Motivation  Methodology  Results  Conclusion  Future work Overview
  22. 22. 22 ELIS – Multimedia Lab Conclusion • Introduced a novel approach for hashtag recommendation, using distributed word representations and a feed forward neural network • Learns semantic and linguistic regularities without requiring careful feature engineering • Can easily take advantage of temporal information • Supports the automatic creation of new hashtags/trends
  23. 23. 23 ELIS – Multimedia Lab  Introduction  Goal  Motivation  Methodology  Results  Conclusion  Future work Overview
  24. 24. 24 ELIS – Multimedia Lab Future Work • Use of more than four days of data • Use word representations from different data sources • Investigate impact of the quality of the word representations created • Investigate impact of the use of DBpedia and Freebase
  25. 25. ELIS – Multimedia Lab

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