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We propose a comprehensive end-to-end pipeline for Twitter hashtagsrecommendation system including data collection, supervised training settingand zero shot training setting. In the supervised training setting, we haveproposed and compared the performance of various deep learning architectures,namely Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) andTransformer Network. However, it is not feasible to collect data for allpossible hashtag labels and train a classifier model on them. To overcome thislimitation, we propose a Zero Shot Learning (ZSL) paradigm for predictingunseen hashtag labels by learning the relationship between the semantic spaceof tweets and the embedding space of hashtag labels. We evaluated variousstate-of-the-art ZSL methods like Convex combination of Semantic Embedding(ConSE), Embarrassingly Simple Zero-Shot Learning (ESZSL) and Deep EmbeddingModel for Zero-Shot Learning (DEM-ZSL) for the hashtag recommendation task. Wedemonstrate the effectiveness and scalability of ZSL methods for therecommendation of unseen hashtags. To the best of our knowledge, this is thefirst quantitative evaluation of ZSL methods to date for unseen hashtagsrecommendations from tweet text.

From Fully Supervised to Zero Shot Settings for Twitter Hashtag Recommendation