Opinion Mining and Emotion Detection in Social Network Data and Student Survey Data in Cloud Environment

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Easwaran, A. (2021). Opinion Mining and Emotion Detection in Social Network Data and Student Survey Data in Cloud Environment. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Recent growth and development of big social media platforms like Twitter, Facebook,Instagram etc. and personal blog sites generate huge amount of unstructured data. Analysis of this data may provide insights into the opinion of people as well as their feelings towards certain subjects, products or services. The process of extracting valuable data from opinions of people to assess their feelings and thoughts is known as opinion mining. Mining the opinions of people has applications in several areas: understanding what people like or dislike is critical for making informed business and political decisions. In this work, we focus on Opinion Mining from Text to suggest Actionable Recommendations. The Actionable Patterns may suggest ways to alter the user's sentiment or emotion to a more positive or desirable state. We apply our method to Twitter Social Network Data, as well as Student Survey Education data. We aim to suggest ways to improve the Social Network Experience and to improve Teaching Methods and Student Learning. We implement and test our system in Scalable Environment with BigData using the Apache Spark platform.

Details
Author

Easwaran, Akshaya

Title

Opinion Mining and Emotion Detection in Social Network Data and Student Survey Data in Cloud Environment

Physical Description

1 online resource (58 pages) : PDF

Date

2021

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Recent growth and development of big social media platforms like Twitter, Facebook,Instagram etc. and personal blog sites generate huge amount of unstructured data. Analysis of this data may provide insights into the opinion of people as well as their feelings towards certain subjects, products or services. The process of extracting valuable data from opinions of people to assess their feelings and thoughts is known as opinion mining. Mining the opinions of people has applications in several areas: understanding what people like or dislike is critical for making informed business and political decisions. In this work, we focus on Opinion Mining from Text to suggest Actionable Recommendations. The Actionable Patterns may suggest ways to alter the user's sentiment or emotion to a more positive or desirable state. We apply our method to Twitter Social Network Data, as well as Student Survey Education data. We aim to suggest ways to improve the Social Network Experience and to improve Teaching Methods and Student Learning. We implement and test our system in Scalable Environment with BigData using the Apache Spark platform.

Genre

masters theses

Subjects--Topics

Computer science

Degree

M.S.

Subject Area

Information Technology

Advisor(s)

Ras, Zbigniew
Tzacheva, Angelina

Committee Members

Park, Albert
Dorodchi, Mohsen

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2021.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Easwaran_uncc_0694N_12683

Permalink

http://hdl.handle.net/20.500.13093/etd:1826