NLP-enabled Recommendation of Hashtags for Covid based Tweets using Hybrid BERT-LSTM Model

Abstract

Hashtags have become a new trend to summarize the feelings, sentiments, emotions, swinging moods, food tastes and much more. It also represents various entities like places, families and friends. It is a way to search and categorize various stuff on social media sites. With the increase in the hashtagging, there is a need to automate it, leading to the term “Hashtag Recommendation”. Also, there are plenty of posts on social media sites which remain untagged. These untagged posts get filtered out while searching and categorizing the data using a label. Such posts do not make any contribution to any helpful insight and remain abandoned. But, if the user of such posts is recommended by labels according to his post, then he may choose one or more of them, thus making the posts labelled. For such cases Hashtag recommendation comes into the picture. Although much research work has been done on Hashtag Recommendation using traditional Deep Learning approaches, not much work has been done using NLP based Bert Embedding. In this paper, we have proposed a model, BELHASH, Bert Embedding based LSTM for Hashtag Recommendation. This task is considered as a Multilabel Classification task as the hashtags are one-hot encoded into multiple binary vectors of zeros and ones using MultiLabelBinarizer. This model has been evaluated on Covid 19 tweets. We have achieved 0.72 accuracy, 0.7 Precision, 0.66 Recall and 0.67 F1-Score. This is the first paper of hashtag recommendation to the best of our knowledge combining Bert embedding with LSTM model and achieving the state of the arts results.