Big Data Stream Analytics for Near Real-Time Sentiment Analysis

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Journal of Computer and Communications

ISSN Print: 2327-5219
ISSN Online: 2327-5227
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"Big Data Stream Analytics for Near Real-Time Sentiment Analysis"
written by Otto K. M. Cheng, Raymond Lau,
published by Journal of Computer and Communications, Vol.3 No.5, 2015

has been cited by the following article(s):

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[1]	Literature review, gaps and research question


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[5]	A Framework for Real-time Sentiment Analysis of Big Data Generated by Social Media Platforms
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[6]	BIG DATA FRAMEWORKS FOR SITES AND PRODUCTS RECOMMENDATION
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[8]	Basic Knowledge Construction Technique to Reduce The Volume of Low-Dimensional Big Data
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[9]	Privacy preserving-aware over big data in clouds using GSA and MapReduce framework
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[10]	A Near Real-Time Approach for Sentiment Analysis Approach Using Arabic Tweets.
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[11]	Sentiment analysis and text categorization of cancer medical records with LSTM
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[12]	A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data
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[13]	Integrated Real-Time Big Data Stream Sentiment Analysis Service
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[14]	Software Agents Role and Predictive Approaches for Online Auctions
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[15]	Conceptual notion for opinion mining from upcoming big data
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[17]	Sentiment Analysis Twitter Data-An Overview
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[18]	Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments
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[19]	A fuzzy long-term investment planning model for a GenCo in a hybrid electricity market considering climate change impacts
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[20]	Data collection and analytics strategies of social networking websites
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[1]	Sentiment analysis and text categorization of cancer medical records with LSTM Journal of Ambient Intelligence and Humanized Computing, 2023 DOI:10.1007/s12652-019-01399-8

[2]	A Framework for Real-time Sentiment Analysis of Big Data Generated by Social Media Platforms 2021 31st International Telecommunication Networks and Applications Conference (ITNAC), 2021 DOI:10.1109/ITNAC53136.2021.9652148

[3]	Big data and sentiment analysis: A comprehensive and systematic literature review Concurrency and Computation: Practice and Experience, 2020 DOI:10.1002/cpe.5671

[4]	Big data and sentiment analysis: A comprehensive and systematic literature review Concurrency and Computation: Practice and Experience, 2020 DOI:10.1002/cpe.5671

[5]	Basic Knowledge Construction Technique to Reduce The Volume of Low-Dimensional Big Data 2020 Fifth International Conference on Informatics and Computing (ICIC), 2020 DOI:10.1109/ICIC50835.2020.9288550

[6]	A spark-based big data analysis framework for real-time sentiment prediction on streaming data Software: Practice and Experience, 2019 DOI:10.1002/spe.2724

[7]	Sentiment analysis and text categorization of cancer medical records with LSTM Journal of Ambient Intelligence and Humanized Computing, 2019 DOI:10.1007/s12652-019-01399-8

[8]	A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data Software: Practice and Experience, 2019 DOI:10.1002/spe.2724

[9]	Map reduce programming model for parallel K-mediod algorithm on hadoop cluster 2017 7th International Conference on Communication Systems and Network Technologies (CSNT), 2017 DOI:10.1109/CSNT.2017.8418514

[10]	A fuzzy long-term investment planning model for a GenCo in a hybrid electricity market considering climate change impacts Information Systems Frontiers, 2017 DOI:10.1007/s10796-016-9707-1

[11]	Conceptual notion for opinion mining from upcoming big data 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), 2017 DOI:10.1109/ICEECCOT.2017.8284591

[12]	Computational Social Networks Lecture Notes in Computer Science, 2016 DOI:10.1007/978-3-319-42345-6_24

[13]	Data collection and analytics strategies of social networking websites 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), 2015 DOI:10.1109/ICGCIoT.2015.7380543

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