CSEIT183687 | Received : 20 July 2018 | Accepted : 05 August 2018 | July-August-2018 [ 3 (6) : 428-434 ] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 3 | Issue 6 | ISSN : 2456-3307 428 Sentiment Analysis Using Parallel Computing Through GPU Harshita Mandloi, Shraddha Masih School of Computer Science and IT, DAVV, Indore, Madhya Pradesh, India 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. Keywords: Parallel Computing, GPU, Social media, K nearest neighbor, SVM I. INTRODUCTION Nowadays social media, blogs, and other media produce a huge amount of data on the world wide web. This huge amount of data contain a critical opinion related to the information that can be used to benefit business and other aspects of commercial and scientific industries. Analysis of user sentiments can help in a business decisions. Sentiments can be categorized into 3 types:- Positive, Negative and Neutral. Graphics Processing Unit has shown better computational performance as compared to the current main-stream multicore Central Processing Unit. Apart from graphics and multimedia processing, high-performance GPUs are also mapped to take care of General Purpose Computing. Machine learning is the current trend in computing that is focused on designing and developing algorithms which allows computers to learn. It can capture characteristics of interest in order to make a prediction for a new data query. The gathered data is considered as training data which illustrate the relationships among the observed variables. Many important patterns can be recognized after applying the learning procedure. Supervised learning is the data mining task of classifying data into labeled data. This work is mainly focused on the classification tools- Support Vector Machine and K Nearest Neighbor. To improve the performance of this algorithm GPU is used with CUDA. Parallel SVM algorithm is implemented on GPU using CUDA framework. The implementation of parallel SVM achieves a great performance using GPU. This software utilizes the parallelism in both data level and task level to maximize the performance of the GPU. II. BACKGROUND A. Parallel Computing- Parallel computing is becoming important day by day. Dividing large task into sub task and assigning these tasks to multiprocessor and executing them concurrently is called parallel processing. Parallel computing is used for high performance computing in the field of data analytics. For the high performance computing the