Data Mining for Emotional Analysis of Big Data

Authors

  • Geda Sai Venkata Abhijith Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India
  • Amit Kumar Vinayak Gundad Assistant Professor, Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India

Keywords:

big data analytics, sentiment analysis, machine-learning algorithms

Abstract

We’re in the midst of the “big data” age right now. Users generate enormous amounts of text data through a variety of means, including social media sites, e-commerce sites, and many kinds of scientific investigations. With this “Text data,” companies may have a better understanding of how the public perceives their brand and use that information to guide future business decisions. As a result, it is imperative for businesses to use sentiment social media data (Big data) to generate forecasts. Open-source big data tools and machine learning techniques are needed to process massive amounts of text data in real time. To this end, we developed a machine learning algorithm-based system for analyzing sentiment in large datasets. Here, the system for text analysis system reviews datasets utilizing the Apache Spark has been built and implemented utilizing the Nave Bayes and Support Vector Machines classification techniques. In addition, accuracy was used to gauge how well the algorithms worked. As demonstrated by these experiments, the Algorithms are quite effective at managing large sentiment datasets. This will be more useful for businesses, governments, & individuals to increase their value.

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References

A. Bifet, “Mining big data in real time,” Inform., vol. 37, no.1, pp.15–20, 2013.

S. Lenka Venkata, “A Survey on Challenges and Advantages in Big Data,” vol. 8491, pp. 115–119, 2015.

J. P. Verma., A. Smita, B. Patel., and P. Atul, “Big Data Analytics: Challenges and Applications for Text, Audio, Video, and Social Media Data,” Int. J. Soft Comput. Artif. Intell. Appl., vol. 5, no. 1, pp. 41–51, 2016.

Ramesh R, Divya G, Divya D, Merin K Kurian, and Vishnuprabha V, “Big Data Sentiment Analysis using Hadoop”, IJIRST, Volume 1, Issue 11, pp. 92-98, 2015.

Nurulhuda Zainuddin, Ali Selamat,” Sentiment Analysis Using Support Vector Machine”, IEEE International Conference on Computer, Communication, and Control Technology (I4CT 2014), Kedah, Malaysia, pp.333-337, 2014.

Rajat Mehta,”Big Data Analytics with Java”, Published by Packt Publishing Ltd, ISBN 978-78728-898-0, UK, 2017.

Kamal Al-Barznji, Atanas Atanassov, “A Framework for Cloud Based Hybrid Recommender System for Big Data Mining”, a journal of “Science, Engineering & Education”, Volume 2, Issue 1, UCTM, Sofia, Bulgaria, pp. 58-65, 2017.

Jason Bell, “Machine Learning: Hands-On for Developers and Technical Professionals”, Published by John Wiley & Sons, Inc., Indianapolis, Indiana, 2015.

Boštjan Kaluža, “Machine Learning in Java”, first published: Published by Packt Publishing Ltd, UK, 2016.

Mohammed Guller, “Big Data Analytics with Spark”, ISBN- 13 (pbk): 978-1-4842-0965-3, 2015.

Benjamin Bengfort and Jenny Kim, “Data Analytics with Hadoop”, Published by O’Reilly Media, Inc., First Edition. USA, 2016.

Nick Pentreath, “Machine Learning with Spark”, Published by Packt Publishing Ltd. BIRMINGHAM – MUMBAI, 2015.

https://spark.apache.org/docs/latest/, Online Feb 2018.

X. Fang and J. Zhan, “Sentiment analysis using product review data,” J. Big Data, pp. 1–14, 2015.

https://machinelearningmastery.com/Online Feb 2018.

Alexandros Baltas, Andreas Kanavos, and Athanasios K. Tsakalidis, “An Apache Spark Implementation for Sentiment Analysis on Twitter Data”, Patras, Greece, Springer International Publishing AG, pp. 15-25, 2017.

Dimitrios Kotzias, Misha Denil, Nando de Freitas, and Padhraic Smyth, “From Group to Individual Labels Using Deep Features”, Publication rights licensed to ACM, KDD, Sydney, NSW, Australia, 10 pages, 2015.

Example figure for sentimental analysis

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Published

04.02.2023

How to Cite

Abhijith, G. S. V. ., & Gundad, A. K. V. . (2023). Data Mining for Emotional Analysis of Big Data. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 271–279. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/2684

Issue

Vol. 11 No. 3s (2023)

Section

Research Article

License

Copyright (c) 2023 Geda Sai Venkata Abhijith, Amit Kumar Vinayak Gundad

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