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Topic Modeling Location-Based Social...

Osailan, Sarah.

Topic Modeling Location-Based Social Media Applications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Topic Modeling Location-Based Social Media Applications.
Author:	Osailan, Sarah.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2020
Description:	125 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-12, Section: B.
Notes:	Advisor: Hilton, Brian.
Contained By:	Dissertations Abstracts International81-12B.
Subject:	Information science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27962084
ISBN:	9798641213309

Topic Modeling Location-Based Social Media Applications.
Osailan, Sarah.

Topic Modeling Location-Based Social Media Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 125 p.

Source: Dissertations Abstracts International, Volume: 81-12, Section: B.

Thesis (Ph.D.)--The Claremont Graduate University, 2020.

This item must not be sold to any third party vendors.

Topic modeling is a technique used in text analysis and mining across various research domains. The number of social media applications and users is increasing daily, and analyzing these data streams provides added value, relevance, and significance for both scholarly and practitioner communities. With increased users, the user-generated content grows, as do the value and information that are extracted from this data stream.The merging of analysis techniques for social media and text enables effective decision making in businesses, because it provides a communication-driven decision support system. This study focuses on combining machine learning and natural language processing techniques to investigate how time affects topics, as derived from tweets. It also examines the impact of observation and information on the decision-making process. The primary objectives of this dissertation were to develop an instantiation and to visualizes topics as derived from user-generated content on Twitter. The study also presents the results of a systematic literature review, which followed a hybrid methodology to illustrate various topic-modeling algorithms. The use of design science research methodology and CRISP-DM methodologies resulted in an instantiation artifact being developed. This served to visualize topic-modeling results using corpus periodization to observe topic-change detection as extracted from Twitter data feeds.

ISBN: 9798641213309Subjects--Topical Terms:

190425
Information science.
Subjects--Index Terms:

Decision Support Systems

Topic Modeling Location-Based Social Media Applications.
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500 $a Source: Dissertations Abstracts International, Volume: 81-12, Section: B.
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520 $a Topic modeling is a technique used in text analysis and mining across various research domains. The number of social media applications and users is increasing daily, and analyzing these data streams provides added value, relevance, and significance for both scholarly and practitioner communities. With increased users, the user-generated content grows, as do the value and information that are extracted from this data stream.The merging of analysis techniques for social media and text enables effective decision making in businesses, because it provides a communication-driven decision support system. This study focuses on combining machine learning and natural language processing techniques to investigate how time affects topics, as derived from tweets. It also examines the impact of observation and information on the decision-making process. The primary objectives of this dissertation were to develop an instantiation and to visualizes topics as derived from user-generated content on Twitter. The study also presents the results of a systematic literature review, which followed a hybrid methodology to illustrate various topic-modeling algorithms. The use of design science research methodology and CRISP-DM methodologies resulted in an instantiation artifact being developed. This served to visualize topic-modeling results using corpus periodization to observe topic-change detection as extracted from Twitter data feeds.
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