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

Abhineshwar Tomar (UGent) , Fréderic Godin (UGent) , Baptist Vandersmissen (UGent) , Wesley De Neve (UGent) and Rik Van de Walle (UGent)
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Abstract
Hashtags are useful for categorizing and discovering content and conversations in online social networks. However, assigning hashtags requires additional user effort, hampering their widespread adoption. Therefore, in this paper, we introduce a novel approach for hashtag recommendation, targeting English language tweets on Twitter. First, we make use of a skip-gram model to learn distributed word representations (word2vec). Next, we make use of the distributed word representations learned to train a deep feed forward neural network. We test our deep neural network by recommending hashtags for tweets with user-assigned hashtags, using Mean Squared Error (MSE) as the objective function. We also test our deep neural network by recommending hash tags for tweets without user-assigned hashtags. Our experimental results show that the proposed approach recommends hashtags that are specific to the semantics of the tweets and that preserve the linguistic regularity of the tweets. In addition, our experimental results show that the proposed approach is capable of generating hash tags that have not been seen before.
Keywords
hashtag recommendation, Rectified Linear Units, distributed word representations, Twitter, word2vec, deep neural networks

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MLA
Tomar, Abhineshwar et al. “Towards Twitter Hashtag Recommendation Using Distributed Word Representations and a Deep Feed Forward Neural Network.” 2014 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI). Delhi, India: IEEE, 2014. 362–368. Print.
APA
Tomar, A., Godin, F., Vandersmissen, B., De Neve, W., & Van de Walle, R. (2014). Towards Twitter Hashtag recommendation using distributed word representations and a deep feed forward neural network. 2014 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI) (pp. 362–368). Presented at the 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI), Delhi, India: IEEE.
Chicago author-date
Tomar, Abhineshwar, Fréderic Godin, Baptist Vandersmissen, Wesley De Neve, and Rik Van de Walle. 2014. “Towards Twitter Hashtag Recommendation Using Distributed Word Representations and a Deep Feed Forward Neural Network.” In 2014 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 362–368. Delhi, India: IEEE.
Chicago author-date (all authors)
Tomar, Abhineshwar, Fréderic Godin, Baptist Vandersmissen, Wesley De Neve, and Rik Van de Walle. 2014. “Towards Twitter Hashtag Recommendation Using Distributed Word Representations and a Deep Feed Forward Neural Network.” In 2014 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 362–368. Delhi, India: IEEE.
Vancouver
1.
Tomar A, Godin F, Vandersmissen B, De Neve W, Van de Walle R. Towards Twitter Hashtag recommendation using distributed word representations and a deep feed forward neural network. 2014 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI). Delhi, India: IEEE; 2014. p. 362–8.
IEEE
[1]
A. Tomar, F. Godin, B. Vandersmissen, W. De Neve, and R. Van de Walle, “Towards Twitter Hashtag recommendation using distributed word representations and a deep feed forward neural network,” in 2014 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), Delhi, India, 2014, pp. 362–368.
@inproceedings{5939643,
  abstract     = {Hashtags are useful for categorizing and discovering content and conversations in online social networks. However, assigning hashtags requires additional user effort, hampering their widespread adoption. Therefore, in this paper, we introduce a novel approach for hashtag recommendation, targeting English language tweets on Twitter. First, we make use of a skip-gram model to learn distributed word representations (word2vec). Next, we make use of the distributed word representations learned to train a deep feed forward neural network. We test our deep neural network by recommending hashtags for tweets with user-assigned hashtags, using Mean Squared Error (MSE) as the objective function. We also test our deep neural network by recommending hash tags for tweets without user-assigned hashtags. Our experimental results show that the proposed approach recommends hashtags that are specific to the semantics of the tweets and that preserve the linguistic regularity of the tweets. In addition, our experimental results show that the proposed approach is capable of generating hash tags that have not been seen before.},
  author       = {Tomar, Abhineshwar and Godin, Fréderic and Vandersmissen, Baptist and De Neve, Wesley and Van de Walle, Rik},
  booktitle    = {2014 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI)},
  isbn         = {9781479930807},
  keywords     = {hashtag recommendation,Rectified Linear Units,distributed word representations,Twitter,word2vec,deep neural networks},
  language     = {eng},
  location     = {Delhi, India},
  pages        = {362--368},
  publisher    = {IEEE},
  title        = {Towards Twitter Hashtag recommendation using distributed word representations and a deep feed forward neural network},
  year         = {2014},
}

Web of Science
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