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

Tomar, Abhineshwar; Godin, Frédéric; Vandersmissen, Baptist; Neve, Wesley De; Walle, Rik Van de

doi:10.1109/icacci.2014.6968557

Cited by 23 publications

(15 citation statements)

References 5 publications

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“…Then, a correlation measurement is used to find the candidate #tags in similar tweets. A personalized method for #tag recommendation based on topical information and collaborative intelligence is introduced by Tomar et al [10]. The topic relevance of hashtags to posts are characterized based on content models.…”

Section: Related Researchesmentioning

confidence: 99%

“…Over the last couple of years, numerous approaches have been introduced for #tag recommendation in microblogging/social networking platforms. The majority of these approaches can be classified in three categories: (1) content-based [9][10][11][12], (2) collaborative filtering-based [10,13] and (3) machine learning-based approaches [14]. The approaches in the first category calculate the similarity of a user's message to the stored messages in a database and recommend #tags to users based on the contextual similarity between them.…”

Section: Introductionmentioning

confidence: 99%

“…The approaches in the first category calculate the similarity of a user's message to the stored messages in a database and recommend #tags to users based on the contextual similarity between them. However, since users' messages are usually short and they cannot convey the intended meaning clearly, it would be very difficult to find suitable #tags [10]. The approaches in the second category analyze the preferences of collaborative users and recommend #tags based on the opinion of users that share common interest with the active user.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach

Jaderyan

Khotanlou

2019

IJE

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A B S T R A C TIn social networking/microblogging environments, #tag is often used for categorizing messages and marking their key points. Also, since some social networks such as twitter apply restrictions on the number of characters in messages, #tags can serve as a useful tool for helping users express their messages. In this paper, a new knowledge-intensive content-based #tag recommendation system is introduced. The proposed system works by integrating structured knowledge in every core component. First, the relevant features, semantic structures and information-content are extracted from messages. Since little information can often be placed in a message, a content enrichment module is introduced to identify information structures that can improve the representation of message. The extracted features are represented by semantic network. Then, a hybrid and multi-layered similarity module identifies the commonalities and differences of the features, semantics and information-content in messages. At the end, #tags are recommended to users based on #tags in contextually similar messages. The system is evaluated on Tweets2011 dataset. The results suggests that the proposed method can recommend suitable #tags in negligible operational time and when little content is available.

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Section: Related Researchesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach

Jaderyan

Khotanlou

2019

IJE

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“…Google has also published pre-trained models as part of their Word2Vec-implementation [29]. Hashtag recommendation has been proposed by learning of word representations with Word2Vec's skip-gram model, and a trained neural network [30]. Another application based on Word2Vec is restaurant recommendations based on similarities between tweets, and specified keywords related to foodborne disease symptoms [1].…”

Section: Literature Reviewmentioning

confidence: 99%

Quality management architecture for social media data

Pääkkönen

Jokitulppo²

2017

J Big Data

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“…In paper [31], they proposed a novel method that recommends hashtags for Tweets written in English Language. They use a skip-gram model for distributed word representation that uses a log-linear classifier to predict words in a range.…”

Section: Related Workmentioning

confidence: 99%

Hashtag the Tweets: Experimental Evaluation of Semantic Relatedness Measures

Asif¹,

Missen²,

Akhtar³

et al. 2016

ijacsa

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Abstract-On Twitter, hashtags are used to summarize topics of the tweet content and to help search tweets. However, hashtags are created in a free style and thus heterogeneous, increasing difficulty of their usage. Therefore, it is important to evaluate that if they really represent the content they are attached with? In this work, we perform detailed experiments to find answer for this question. In addition to this, we compare different semantic relatedness measures to find this similarity between hashtags and tweets. Experiments are performed using ten different measures and Adapted Lesk is found to be the best.

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

Cited by 23 publications

References 5 publications

Automatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach

Automatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach

Quality management architecture for social media data

Hashtag the Tweets: Experimental Evaluation of Semantic Relatedness Measures

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