Sentiment Analysis Using Parallel Computing Through GPU

Authors

  • Harshita Mandloi  School of Computer Science and IT, DAVV, Indore, Madhya Pradesh, India
  • Shraddha Masih  School of Computer Science and IT, DAVV, Indore, Madhya Pradesh, India

Keywords:

Parallel Computing, GPU, Social media, K nearest neighbor, SVM

Abstract

Parallel Computing is becoming important in the field of computer science and is proven as a high-performance solution. Over the couple of years, GPU has gained an important place in the field of high-performance computing. Social media is expanding at present and becoming important in society. Social network sites allow users to communicate with people in the network by sharing posts, images, videos, status. The proposed system gathers the information from the social media websites and performs the sentiment analysis on the social media data using GPU. The work concentrates on recognizing the sentiment information from the text reviews and using that to identify the items. The aim of this paper is to do analytics on social media data. Analysis is done on the data using K Nearest Neighbor algorithms and Support Vector Machine algorithm on the GPU.

References

  1. Li, Shengren, and Nina Amenta. "Brute-force k-nearest neighbors search on the GPU." International Conference on Similarity Search and Applications. Springer, Cham, 2015.
  2. Navarro, Cristobal A., Nancy Hitschfeld-Kahler, and Luis Mateu. "A survey on parallel computing and its applications in data-parallel problems using GPU architectures." Communications in Computational Physics 15.2 (2014): 285-329.
  3. Accelerating Machine Learning Algorithms in Python Patrick Reilly, Leiming Yu and David Kaeli, Department of Electrical and Computer Engineering Northeastern University, Boston, MA [email protected],fylm,[email protected].
  4. Chen, Linchuan, Xin Huo, and Gagan Agrawal. "Accelerating MapReduce on a coupled CPU GPU architecture." Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society Press, 2012.
  5. Li, Peilong, et al. "Heterospark: A heterogeneous cpu/gpu spark platform for machine learning algorithms." Networking, Architecture and Storage (NAS), 2015 IEEE International Conference on. IEEE, 2015.
  6. Bello-Orgaz, Gema, Jason J. Jung, and David Camacho. "Social big data: Recent achievements and new challenges." Information Fusion 28 (2016): 45-59.
  7. Huq, Mohammad Rezwanul, Ahmad Ali, and Anika Rahman. "Sentiment analysis on Twitter data using KNN and SVM." Int J Adv Comput Sci Appl 8.6 (2017): 19-25.
  8. Li, Qi, et al. "GPUSVM: a comprehensive CUDA based support vector machine package." Open Computer Science1.4 (2011): 387-405.
  9. Thambawita, D. R. V. L. B., Roshan Ragel, and Dhammika Elkaduwe. "To use or not to use: Graphics processing units (GPUs) for pattern matching algorithms." Information and Automation for Sustainability (ICIAfS), 2014 7th International Conference on. IEEE, 2014.

Downloads

Published

2018-07-30

Issue

Section

Research Articles

How to Cite

[1]
Harshita Mandloi, Shraddha Masih, " Sentiment Analysis Using Parallel Computing Through GPU, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 6, pp.428-434, July-August-2018.