Forecasting of Twitter hashtag temporal dynamics using locally weighted projection regression
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Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Popularity of social networks opens great opportunities for market such as advertisement. Using hashtags increasingly used in twits helps us to realize popular topics on the internet. Since most of new hashtags become popular and then fade away quickly, there is a limited time to predict the trend. Therefore, this paper proposes a fast incremental method to forecast the rate of the used hashtags in hour like time series. Two main parts for forecasting system are applied Preprocessing and Supervised Learning. Normalization is one of most popular preprocessing of dataset also proposed to have larger dataset. Moreover, the efficiency of the system under changing number of input (number of past hours from hashtag history) and output (number of next hours which is going to be predicted) are evaluated. Locally Weighted Projection Regression as one of the most powerful machine learning methods with no metaparameter are applied in this paper as real-Time learning method. The performance of the system is verified by implementation of 'Volume Time Series of Memetracker Phrases and Twitter Hashtags'. The results show that the errors of forecasting system are good enough to understand the trend of the hashtag. © 2017 IEEE.
Açıklama
Akdeniz University;IARES;IEEE
2017 International Conference on Engineering and Technology, ICET 2017 -- 21 August 2017 through 23 August 2017 -- -- 135161
2017 International Conference on Engineering and Technology, ICET 2017 -- 21 August 2017 through 23 August 2017 -- -- 135161
Anahtar Kelimeler
Hashtag, Locally Weighted Projection Regression, Machine Learning, Supervised Learning, Time Series Forecasting, Twitter
Kaynak
Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
2018-January
