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dc.contributor.authorOmela Manases, Malachi
dc.contributor.authorThiga, Moses
dc.contributor.authorMasese, Nelson
dc.date.accessioned2024-04-29T12:16:09Z
dc.date.available2024-04-29T12:16:09Z
dc.date.issued2022
dc.identifier.issn2663-0346
dc.identifier.urihttp://ir.kabarak.ac.ke/handle/123456789/1532
dc.description.abstractPublic participation is important because it helps to close the gap between the public, private sector and the government. However, a successful devolution process in Kenya is hampered by a lack of/inadequate public participation in county governments. Communications gaps are one of the arguments made for this development. The main objective of the study was to develop a sentiment analysis model for use in public participation forums in County Governments in Kenya. The study was conducted through the design thinking process. The population of interest of this study comprised of county management and staff also area residents in Nakuru, Busia and Baringo counties who have participated in public participation forums before. The Bidirectional Encoder Representations from Transformers (BERT) approach was used to create the cloud NLP package and obtain user sentiment magnitudes for the sentiment analysis model. Following that, cross validation was utilized to assess the performance indicators during the design stage, and users took part in the model's assessment. The overall conclusion of validation is that the model performed as expected and recorded instrumental results in increasing effective public participation in county governments in Kenya and strengthen the devolution process. This study recommends that the model can be cascaded to all the counties in Kenya to improve the efficiency of public participation.en_US
dc.language.isoenen_US
dc.publisherEast African Scholars Journal of Engineering and Computer Sciencesen_US
dc.subjectPublic participationen_US
dc.subject(BERT)en_US
dc.subjectSentiment Analysis Modelen_US
dc.titleSentiment Analysis Model for Public Participation Forums in County Governmentsen_US
dc.typeArticleen_US


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