TY - GEN
T1 - Using Topic Modeling to Find Hidden Structures in the Language of Sesotho sa Leboa
AU - Masethe, Hlaudi Daniel
AU - Masethe, Mosima Anna
AU - Ojo, Sunday O.
AU - Owolawi, Pius A.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Calls from customers are a great way for service providers to get input about their application process. These calls may also provide a plethora of previously undiscovered information about the queries and concerns of customers. Unfortunately, it might be difficult to properly examine these call data because they are usually unstructured. This study aims to extract valuable customer insights from recorded customer calls to a South African government workers medical scheme (GEMS) application procedure by utilizing Topic Modeling techniques, a branch of Natural Language Processing. The objective of the research is to examine popular Topic Modeling algorithms such as LDA, BERTOPIC, LSA, LSI, and HDP for call content analysis, and categorization, to gain insights into human behaviors and experiences. The research intends to give the business a thorough grasp of client wants, interests, and concerns by utilizing the power of these algorithms, ultimately enabling more efficient decision-making processes. Natural language processing makes heavy use of topic modeling approaches to derive subjects from unstructured text input. A widely used method in topic modeling allows topics to be automatically extracted from a large corpus of textual materials. BERTopic, a transformer-based architecture, outperforms all other conventional algorithms with a greater accuracy of 91%.
AB - Calls from customers are a great way for service providers to get input about their application process. These calls may also provide a plethora of previously undiscovered information about the queries and concerns of customers. Unfortunately, it might be difficult to properly examine these call data because they are usually unstructured. This study aims to extract valuable customer insights from recorded customer calls to a South African government workers medical scheme (GEMS) application procedure by utilizing Topic Modeling techniques, a branch of Natural Language Processing. The objective of the research is to examine popular Topic Modeling algorithms such as LDA, BERTOPIC, LSA, LSI, and HDP for call content analysis, and categorization, to gain insights into human behaviors and experiences. The research intends to give the business a thorough grasp of client wants, interests, and concerns by utilizing the power of these algorithms, ultimately enabling more efficient decision-making processes. Natural language processing makes heavy use of topic modeling approaches to derive subjects from unstructured text input. A widely used method in topic modeling allows topics to be automatically extracted from a large corpus of textual materials. BERTopic, a transformer-based architecture, outperforms all other conventional algorithms with a greater accuracy of 91%.
KW - BERTopic
KW - HDP
KW - LDA
KW - LSI
KW - Language models
KW - Term Frequency-Inverse Document
KW - Topic Modeling
UR - http://www.scopus.com/inward/record.url?scp=85192243273&partnerID=8YFLogxK
U2 - 10.1109/ICTAS59620.2024.10507132
DO - 10.1109/ICTAS59620.2024.10507132
M3 - Conference contribution
AN - SCOPUS:85192243273
T3 - 2024 Conference on Information Communication Technology and Society, ICTAS 2024 - Proceedings
SP - 75
EP - 81
BT - 2024 Conference on Information Communication Technology and Society, ICTAS 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th Conference on Information Communication Technology and Society, ICTAS 2024
Y2 - 7 March 2024 through 8 March 2024
ER -