CFP last date
22 April 2024
Reseach Article

A New Recommender System for Hashtags

by Varun Ranganathan, Tanya Mehrotra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 32
Year of Publication: 2018
Authors: Varun Ranganathan, Tanya Mehrotra
10.5120/ijca2018916871

Varun Ranganathan, Tanya Mehrotra . A New Recommender System for Hashtags. International Journal of Computer Applications. 180, 32 ( Apr 2018), 1-6. DOI=10.5120/ijca2018916871

@article{ 10.5120/ijca2018916871,
author = { Varun Ranganathan, Tanya Mehrotra },
title = { A New Recommender System for Hashtags },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 180 },
number = { 32 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number32/29247-2018916871/ },
doi = { 10.5120/ijca2018916871 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:02:25.522240+05:30
%A Varun Ranganathan
%A Tanya Mehrotra
%T A New Recommender System for Hashtags
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 32
%P 1-6
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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.

References
  1. Godin, Frderic, Viktor Slavkovikj, Wesley De Neve, Benjamin Schrauwen, and Rik Van de Walle. ‘Using topic models for twitter hashtag recommendation.’ In Proceedings of the 22nd International Conference on World Wide Web, pp. 593-596. ACM, 2013.
  2. Krokos, Eric, Hanan Samet, and Jagan Sankaranarayanan. ‘A look into twitter hashtag discovery and generation.’ In Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, pp. 49-56. ACM, 2014.
  3. Kywe, Su, Tuan-Anh Hoang, Ee-Peng Lim, and Feida Zhu. ‘On recommending hashtags in twitter networks’. Social Informatics (2012): 337-350
  4. Sriram, Bharath, Dave Fuhry, Engin Demir, Hakan Ferhatosmanoglu, and Murat Demirbas. ‘Short text classification in twitter to improve information filtering.’ In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 841-842. ACM, 2010.
  5. Popescu, Ana-Maria, Marco Pennacchiotti, and Deepa Paranjpe. ‘Extracting events and event descriptions from twitter.’ In Proceedings of the 20th international conference companion on World wide web, pp. 105-106. ACM, 2011.
  6. Bengio, Yoshua (2009). ‘Learning Deep Architectures for AI’. Foundations and Trends in Machine Learning. 2 (1): 1127. doi:10.1561/2200000006.
  7. Schmidhuber, J. (2015). ‘Deep Learning in Neural Networks: An Overview’. Neural Networks. 61: 85117. arXiv:1404.7828 Freely accessible. doi:10.1016/j.neunet.2014.09.003. PMID 25462637.
  8. Szegedy, Christian; Toshev, Alexander; Erhan, Dumitru (2013). ‘Deep neural networks for object detection’. Advances in Neural Information Processing Systems.
  9. Peter Norvig. ‘Spelling Corrector in Python 3’. Copyright (c) 2007-2016 MIT license: www.opensource.org/licenses/mitlicense. php
  10. Jeffrey Pennington, Richard Socher, Christopher D. Manning. ‘GloVe: Global Vectors for Word Representation’.
  11. Raja Jurdak , Kun Zhao, Jiajun Liu, Maurice AbouJaoude, Mark Cameron, David Newth. ‘Understanding Human Mobility from Twitter’.
Index Terms

Computer Science
Information Sciences

Keywords

Naive Bayes Support Vector Machine Artificial Neural Network Machine Learning