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Title: A spark-based big data analysis framework for real-time sentiment prediction on streaming data

Authors:
ORCiD logo [1]
  1. Department of Software Engineering, Faculty of Technology, Manisa Celal Bayar University, Manisa Turkey
Publication Date:
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE), Power Systems Engineering Research and Development (R&D) (OE-10)
OSTI Identifier:
1529994
Grant/Contract Number:  
3170030
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Software, Practice and Experience
Additional Journal Information:
Journal Name: Software, Practice and Experience; Journal ID: ISSN 0038-0644
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Kılınç, Deniz. A spark-based big data analysis framework for real-time sentiment prediction on streaming data. United Kingdom: N. p., 2019. Web. doi:10.1002/spe.2724.
Kılınç, Deniz. A spark-based big data analysis framework for real-time sentiment prediction on streaming data. United Kingdom. doi:10.1002/spe.2724.
Kılınç, Deniz. Thu . "A spark-based big data analysis framework for real-time sentiment prediction on streaming data". United Kingdom. doi:10.1002/spe.2724.
@article{osti_1529994,
title = {A spark-based big data analysis framework for real-time sentiment prediction on streaming data},
author = {Kılınç, Deniz},
abstractNote = {},
doi = {10.1002/spe.2724},
journal = {Software, Practice and Experience},
number = ,
volume = ,
place = {United Kingdom},
year = {2019},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on June 26, 2020
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