Data Parallelism : A New Approach in Prediction Systems
DOI:
https://doi.org/10.26438/ijcse/v6i9.211214Keywords:
Fork Join Pool, Open NLP, Sentiment Analysis, Data ParallelismAbstract
The day-by-day growing data can compromise the performance of the prediction system, because its obvious that the growing data will require more storage and the system will also consume more time for its processing. In prediction system, testing is part where time is consumed. If the entire data is given to the test model, it will run for the entire input size, and becomes time consuming. For this effective reduction strategy for processing time of testing must be introduced. To reduce this processing time introducing parallelism concept can help. The framework used here is based on fork join pool. In this the input size is divided into parts which are small enough to be processed and then the divided parts are given for testing. Thus reducing the time consumed in testing, and making it better than the other system.
References
[1] X. Li, Q. Peng, Z. Sun, L. Chai, and Y. Wang, “Predicting social emotions from readers perspective”, IEEE Transactions on Affective Computing, no. 1, pp. 1-1, 2017.
[2] Anshuman, S. Rao, and M. Kakkar, “A rating approach based on sentiment analysis,” Proceeding of 2017 7th International Conference on Cloud Computing, Data Science and Engineering Confluence, pp. 557-562, 2017
[3] S. Khatri and A. Srivastava, “Using sentimental analysis in prediction of stock market Investment , ” proceeding of 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 566-569,2016.
[4] Y. Shen, W. R. Yazhi Gao, and Z. Xiong, “Convolutional neural network based sentiment analysis using adaboost combination,” Proceeding of 2016 International Joint Conference on Neural Networks (IJCNN), pp. 1333-1338, 2016.
[5] R.Hong, M. Chuan He, Yong Ge, and X. Wu, “User vitality ranking and prediction in social networking services: a dynamic network perspective,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 6, pp. 1343-1356, June 2017.
[6] I. Smith, “A parallel artificial neural network implementation,” Proceedings of The National Conference On Undergraduate Research (NCUR, pp. 1-4, April 2006.
[7] HS.Kisan, HA.Kisan, and AP.Suresh, “Collective intelligence and sentimental analysis of twitter data by using standfordnlp libraries with software as a service saas,” Proceeding of 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-4, 2016.
[8] R.Krchnavy, M.Krchnavy, and M. Simko, “Sentiment analysis of social network posts in slovak language,” 2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 20-25, July 2017.
[9] D. Cenni, G. P. Paolo Nesi, and I. Zaza, “Twitter vigilance: a multi-user platform for cross-domain twitter data analytics, nlp and sentiment analysis,” , Proceeding of 2017 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation ( Smart World / SCALCOM / UIC / ATC / CBD Com / IOP / SCI), pp. 1- 8, Aug 2017.
[10] F. Nausheen and S. H. Begum, “Sentiment analysis to predict election results using python,” Proceeding of 2018 2nd International Conference on Inventive Systems and Control (ICISC), pp. 1259-1262, Jan 2018.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
