Publication:
Sentiment analysis of customer tweets related to philippine internet service providers

Simple item page
dc.contributor.authorAcenas, Jennifer C.
dc.contributor.authorCasela, Jericho A.
dc.contributor.authorCerenado, Armalyn B.
dc.contributor.authorEstacio, Anthony N.
dc.date.accessioned2025-09-06T07:08:12Z
dc.date.available2025-09-06T07:08:12Z
dc.date.issued2023-04
dc.descriptionFull text.
dc.description.abstractThe researchers conducted a sentiment analysis on customer tweets which are related to the two chosen Philippine internet service providers to determine the company's efficiency in delivering the best to their customers. It aimed to determine the preprocessing techniques to be used on the extracted dataset; to train the sentiment analysis model using machine learning algorithms; and to evaluate the performance of the models in terms of accuracy. The research design of this study was experimental approach. The researchers gathered tweets as data by using RapidMiner and Twitter API Three algorithms were used in this study: Naive Bayes with Laplace Smoothing, Support Vector Machine with Kernel Types and K-Nearest Neighbor with K-values as parameters. Both corpora showed that Support Vector Machine with kernel type Radial Basis Function had the highest accuracy and was concluded to be the best algorithm and parameter. Internet Service Provider 1 has higher accuracy with 0.8581 while Internet Service Provider 2 have an accuracy score of 0.7027 therefore the best corpus was Internet Service Provider 1.
dc.identifier.citationAcenas, J. C., Casela, J. A., Cernado, A. B., & Estacio, A. N. (2023) Sentiment analysis of customer tweets related to philippine internet service providers [Unpublished Undergraduate thesis]. Don Mariano Marcos Memorial State Univeristy - South La Union Campus, Agoo, La Union.
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/346
dc.language.isoen_US
dc.publisherDon Mariano Marcos Memorial State Univeristy - South La Union Campus
dc.sdgSDG 16
dc.titleSentiment analysis of customer tweets related to philippine internet service providers
dc.typeThesis
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
UT-SLUC-2023-CCS-BSCS-AcenasJ-Ft.pdf
Size:
5.75 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Undergraduate Theses