The hashtag recommendation systems on Twitter have largely focused on analyzing the text content of tweets. In this work, we modify the state-of-the-art existing natural language processing (NLP) technique and deeply ingrain socio-temporal techniques into the overall process, to model a novel hashtag recommendation system. The social aspect of the system aims to make use of the hashtags generated by familiar individuals possess, as well as, the hashtags used by the individual at the past (profile). The temporal aspect aims to age the tweets, thereby ensuring that the more recent hashtags receive higher weights in the process of recommendation. The NLP technique is modified to offer an initial score based upon text embedding of hashtags, and a socio-temporal function and a burst function are applied to generate a final relevance score for hashtags towards a given tweet. The hashtags with top-K relevance scores are recommended to the user. © Springer Nature Switzerland AG 2019.