EmHash: Hashtag Recommendation using Neural Network based on BERT Embedding

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Kaviani Mohadeseh; RAHMANI HOSSEIN

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2020 | Issue:6

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Title

EmHash: Hashtag Recommendation using Neural Network based on BERT Embedding

Author(s)

Kaviani Mohadeseh | RAHMANI HOSSEIN

Keywords

hashtag recommendation

neural network

deeplearning

clustering

cosine similarity

BERT embedding

Abstract

Social media like Twitter have become very popular in recent decades. Hashtags are new kind of metadata which make non-structured tweets into searchable semistructured content. There are varied previous methods which recommend hashtags for new tweets. However, to the best of our knowledge, there is no previous word that uses BERT embedding for this purpose. In this paper, we propose a new method called EmHash that uses neural network based on BERT embedding to recommend new hashtags for each tweet. Unlike other word embeddings, BERT embedding constructs different vectors for the same word in different contexts. Emhash succeeded in outperforming three methods LDA, SVM, and TTM with respect to recall measure.

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APA: Copy

Kaviani, Mohadeseh, & RAHMANI, HOSSEIN. (2020). EmHash: Hashtag Recommendation using Neural Network based on BERT Embedding. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949235/en

Vancouver: Copy

Kaviani Mohadeseh, RAHMANI HOSSEIN. EmHash: Hashtag Recommendation using Neural Network based on BERT Embedding. 2020. Available from: https://sid.ir/paper/949235/en

IEEE: Copy

Mohadeseh Kaviani, and HOSSEIN RAHMANI, “EmHash: Hashtag Recommendation using Neural Network based on BERT Embedding,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2020, [Online]. Available: https://sid.ir/paper/949235/en

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