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A New Recommender System for Hashtags

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Varun Ranganathan, Tanya Mehrotra

Varun Ranganathan and Tanya Mehrotra. A New Recommender System for Hashtags. International Journal of Computer Applications 180(32):1-6, April 2018. BibTeX

	author = {Varun Ranganathan and Tanya Mehrotra},
	title = {A New Recommender System for Hashtags},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2018},
	volume = {180},
	number = {32},
	month = {Apr},
	year = {2018},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2018916871},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Hashtags are one of the trendiest methods to label content on the internet. They are used to cluster data which in turn, results in its easier retrieval. Twitter is the main source from where the use of hashtags rocketed [1]. With the generation of large amounts of microposts, there is a need for effective categorization and search of the data. In this paper, we aim to propose a new method of recommending hashtags for a given message in a tweet so that hashtags can be effectively used by both data analysts and common users of the twitter platform. We will use various machine learning and deep learning concepts, and later combine them all together in one model to obtain a set of relevant hashtags for tweets fetched at real time. The results obtained by this new model resulted in far better recommendation than when each of the models were implemented separately.


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Naive Bayes, Support Vector Machine, Artificial Neural Network, Machine Learning