Original scientific paper

https://doi.org/10.17559/TV-20180123005000

High Performance Twitter Sentiment Analysis Using CUDA Based Distance Kernel on GPUs

Ferhat Bozkurt orcid.org/0000-0003-0088-5825 ; Department of Computer Engineering, Faculty of Engineering, Ataturk University, Erzurum, 25240, Turkey
Önder Çoban orcid.org/0000-0001-9404-2583 ; Department of Computer Engineering, Faculty of Engineering, Adıyaman University, Adıyaman, 02040, Turkey
Faruk Baturalp Günay orcid.org/0000-0001-5472-3608 ; Department of Computer Engineering, Faculty of Engineering, Ataturk University, Erzurum, 25240, Turkey
Şeyma Yücel Altay orcid.org/0000-0002-7460-3993 ; Department of Computer Engineering, Faculty of Engineering, Ataturk University, Erzurum, 25240, Turkey

Abstract

Sentiment analysis techniques are widely used for extracting feelings of users in different domains such as social media content, surveys, and user reviews. This is mostly performed by using classical text classification techniques. One of the major challenges in this field is having a large and sparse feature space that stems from sparse representation of texts. The high dimensionality of the feature space creates a serious problem in terms of time and performance for sentiment analysis. This is particularly important when selected classifier requires intense calculations as in k-NN. To cope with this problem, we used sentiment analysis techniques for Turkish Twitter feeds using the NVIDIA’s CUDA technology. We employed our CUDA-based distance kernel implementation for k-NN which is a widely used lazy classifier in this field. We conducted our experiments on four machines with different computing capacities in terms of GPU and CPU configuration to analyze the impact on speed-up.

Keywords

CUDA; k-NN; LDA; parallel computing; sentiment analysis; twitter

Hrčak ID:

226002

URI

https://hrcak.srce.hr/226002

Publication date:

8.10.2019.

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