Text Classification Using SVM Enhanced by Multithreading and CUDA

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Soumick Chatterjee 1,* Pramod George Jose 2 Debabrata Datta 3

1. Otto von Guericke University, Magdeburg, Germany

2. Department of Cyber Security and Networks, Amrita University, Coimbatore, India

3. Department of Computer Science, St. Xavier’s College (Autonomous), Kolkata, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2019.01.02

Received: 8 Aug. 2018 / Revised: 10 Sep. 2018 / Accepted: 17 Oct. 2018 / Published: 8 Jan. 2019

Index Terms

Stemming, lemmatization, SVM, mutithreading, CUDA


With the sudden growth of the internet and digital documents available on the web, the task of organizing text data has become a major problem. In recent times, text classification has become one of the main techniques for organizing text data. The idea behind text classification is to classify a given piece of text to a predefined class or category. In the present research work, SVM has been used with linear kernel using the One-V-Rest strategy. The SVM is trained using various data sets collected from various sources. It may so happen that some particular words were not so common around 5-6 years ago, but are currently prevalent due to recent trends. Similarly, new discoveries may result in the coinage of new words. This process can also be applied to text blogs which can be crawled and then analyzed. This technique should in theory be able to classify blogs, tweets or any other document with a significant amount of accuracy. In any text classification process, preprocessing phase takes the most amount of time – cleaning, stemming, lemmatization etc. Hence, the authors have used a multithreading approach to speed up the process. The authors further tried to improve the processing time of the algorithm using GPU parallelism using CUDA.

Cite This Paper

Soumick Chatterjee, Pramod George Jose, Debabrata Datta, "Text Classification Using SVM Enhanced by Multithreading and CUDA", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.1, pp. 11-23, 2019.DOI: 10.5815/ijmecs.2019.01.02


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