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Integrated Real-Time Social Media Sentiment Analysis Service Using a Big Data Analytic Ecosystem

Aring, Danielle C
http://orcid.org/0000-0001-8862-8190
http://rave.ohiolink.edu/etdc/view?acc_num=csu1494359605127555

Abstract Details

2017, Master of Computer and Information Science, Cleveland State University, Washkewicz College of Engineering.
Big data analytics are at the center of modern science and business. Our social media networks, mobile devices and enterprise systems generate enormous volumes of it on a daily basis. This wide range of availability provides many organizations in every field opportunities to discover valuable intelligence for critical decision-making. However, traditional analytic architectures are insufficient to handle unprecedentedly big volume of data and complexity of data processing. This thesis presents an analytic framework to combat unprecedented scale of big data that performs data stream sentiment analysis effectively in real time. The work presents a Social Media Big Data Sentiment Analytics Service System (SMBDSASS). The architecture leverages Apache Spark stream data processing framework, coupled with a NoSQL Hive big data ecosystem. Two sentiment analysis models were developed; the first, a topic based model, given user provided topic or person of interest sentiment (opinion) analysis was performed on related topic sentences in a tweet stream. The second, an aspect (feature) based model given user provided product of interest and related product features aspect (feature) analysis was performed on reviews containing important feature terms. The experimental results of the proposed framework using real time tweet stream and product reviews show comparable improvements from the results of the existing literature, with 73% accuracy for topic-based sentiment model, and 74% accuracy for aspect (feature) based sentiment model. The work demonstrated that our topic and aspect based sentiment analysis models on the real time stream data processing framework using Apache Spark and machine learning classifiers coupled with a NoSQL big data ecosystem offer an efficient, scalable, real-time stream data-processing alternative for the complex multiphase sentiment analysis over common batch data mining frameworks.
Sun Sunnie Chung, Ph.D. (Committee Chair)
Yongjigan Fu, Ph.D. (Committee Member)
Ifthkar Sikder, Ph.D. (Committee Member)
Computer Science
big data analytics, sentiment analysis, stream data-processing

Recommended Citations

Citations

  • Aring, D. C. (2017). Integrated Real-Time Social Media Sentiment Analysis Service Using a Big Data Analytic Ecosystem [Master's thesis, Cleveland State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=csu1494359605127555

    APA Style (7th edition)

  • Aring, Danielle. Integrated Real-Time Social Media Sentiment Analysis Service Using a Big Data Analytic Ecosystem. 2017. Cleveland State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=csu1494359605127555.

    MLA Style (8th edition)

  • Aring, Danielle. "Integrated Real-Time Social Media Sentiment Analysis Service Using a Big Data Analytic Ecosystem." Master's thesis, Cleveland State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=csu1494359605127555

    Chicago Manual of Style (17th edition)

csu1494359605127555
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© 2017, all rights reserved.
This open access ETD is published by Cleveland State University and OhioLINK.