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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Osailan, Sarah Yousif"],"institution":null,"degree_name":"Information Systems and Technology, PhD","degree_level":"Restricted to Claremont Colleges Dissertation","degree_discipline":"Center for Information Systems and Technology","degree_department":null,"school":null,"contributors":["Anthony Corso","Lorne Olfman"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-01-01T08:00:00Z","date_published":"2020-01-01T08:00:00Z","updated_at":"2026-07-24T01:40:28Z","subjects":["Decision Support Systems","Information Systems & Technology","Natural Language Processing","Social Media Analysis","Text Mining","Topic Modeling"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/651","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Anthony Corso","Lorne Olfman"]},{"key":"dc:creator","label":"Author","values":["Osailan, Sarah Yousif"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-01-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Center for Information Systems and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Restricted to Claremont Colleges Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Information Systems and Technology, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Decision Support Systems","Information Systems & Technology","Natural Language Processing","Social Media Analysis","Text Mining","Topic Modeling"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/651"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Topic Modeling Location-Based Social Media Applications"]}]}],"canonical_facts":{"dc:contributor":["Anthony Corso","Lorne Olfman"],"dc:creator":["Osailan, Sarah Yousif"],"dc:date.available":["2023-01-01T08:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/651"],"dc:subject":["Decision Support Systems","Information Systems & Technology","Natural Language Processing","Social Media Analysis","Text Mining","Topic Modeling"],"dc:title":["Topic Modeling Location-Based Social Media Applications"],"thesis:degree_discipline":["Center for Information Systems and Technology"],"thesis:degree_level":["Restricted to Claremont Colleges Dissertation"],"thesis:degree_name":["Information Systems and Technology, PhD"]},"updated_at":"2026-07-24T01:40:28Z"}