Big data driven e-government framework in Nigeria

Ogbuju, Emeka; Taiwo, Kayode; Ejiofor, Virginia E.; Onyesolu, Moses Okechukwu

doi:10.4314/bajopas.v11i2.35

Cited by 3 publications

(3 citation statements)

References 2 publications

(2 reference statements)

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“…This constraint is eliminated when data about the operations of multiple organizations are cultivated and harvested in a single database backend, under the control of a common application. Data cultivation and harvesting is a concept first introduced in [43] to refer to growing data over some time, to make it become big enough for predictive analytics. The data may belong to different organisations but they are grown using a single sign-on application.…”

Section: Methodsmentioning

confidence: 99%

A data model for enhanced data comparability across multiple organizations

PhD

Ogbuju

2020

J Big Data

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Organizations may be related in terms of similar operational procedures, management, and supervisory agencies coordinating their operations. Supervisory agencies may be governmental or non-governmental but, in all cases, they perform oversight functions over the activities of the organizations under their control. Multiple organizations that are related in terms of oversight functions by their supervisory agencies, may differ significantly in terms of their geographical locations, aims, and objectives. To harmonize these differences such that comparative analysis will be meaningful, data about the operations of multiple organizations under one control or management can be cultivated, using a uniform format. In this format, data is easily harvested and the ease with which it is used for cross-population analysis, referred to as data comparability is enhanced. The current practice, whereby organizations under one control maintain their data in independent databases, specific to an enterprise application, greatly reduces data comparability and makes cross-population analysis a herculean task. In this paper, the collocation data model is formulated as consisting of big data technologies beyond data mining techniques and used to reduce the heterogeneity inherent in databases maintained independently across multiple organizations. The collocation data model is thus presented as capable of enhancing data comparability across multiple organizations. The model was used to cultivate the assessment scores of students in some schools for some period and used to rank the schools. The model permits data comparability across several geographical scales among which are: national, regional and global scales, where harvested data form the basis for generating analytics for insights, hindsight, and foresight about organizational problems and strategies.

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Section: Methodsmentioning

confidence: 99%

A data model for enhanced data comparability across multiple organizations

PhD

Ogbuju

2020

J Big Data

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“…In the case of the pure Hausa dataset SVM yields the best result. Similarly, [36] observed a scarcity of publicly accessible sentiment lexicons for low-resource languages, particularly notable in the Igbo language. In response, they advocated for the development of a comprehensive sentiment lexicon tailored for Igbo, designed to serve as a foundational resource for sentiment analysis in this linguistically underrepresented context.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Transformer Models for Sentiment Analysis in Low-Resource Languages

Aliyu,

Sarlan,

Danyaro

et al. 2024

IJACSA

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The analysis of sentiments expressed on social media platforms is a crucial tool for understanding user opinions and preferences. The large amount of the texts found on social media are mostly in different languages. However, the accuracy of sentiment analysis in these systems faces different challenges in multilingual low-resource settings. Recent advancements in deep learning transformer models have demonstrated superior performance compared to traditional machine learning techniques. The majority of preceding works are predominantly constructed on the foundation of monolingual languages. This study presents a comparative analysis that assesses the effectiveness of transformer models, for multilingual lowresource languages sentiment analysis. The study aims to improve the accuracy of the existing baseline performance in analyzing tweets written in 12 low-resource African languages. Four widely used start-of-the-art transformer models were employed. The experiment was carried out using standard datasets of tweets. The study showcases AfriBERTa as a robust performer, exhibiting superior sentiment analysis capabilities across diverse linguistic contexts. It outperformed the established benchmarks in both SemEval-2023 Task 12 and AfriSenti baseline. Our framework achieves remarkable results with an F1-score of 81% and an accuracy rate of 80.9%. This study provides validation of the framework's robustness in the domain of sentiment analysis across a low-resource linguistics context. our research not only contributes a comprehensive sentiment analysis framework for low-resource African languages but also charts a roadmap for future enhancements. Emphasize the ongoing pursuit of adaptability and robustness in sentiment analysis models for diverse linguistic landscapes.

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“…Also, Ogbuju et. al [11] determined the public opinion of the Nigerian police force and the Nigerian army using datasets collected from their social media accounts with the help of an e-government big data-driven framework. In the same vein, Oyebode and Orji [13] applied sentiment analysis to analyze public opinions during the 2019 Nigeria presidential election to determine who would win the election using social media comments.…”

Section: Introductionmentioning

confidence: 99%

The Sentiment Analysis of EndSARS Protest in Nigeria

Ogbuju

Mpama²,

Oluwafemi

et al. 2022

J. Appl. Artif. Intell.

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The extraction of public opinions from online communication platforms can serve several purposes in corporate institutions, state politics, and governance. The analysis of these opinions may be useful for both immediate business decision making and professional planning. This analysis is becoming relevant in managing social movements and digital activism by applying computational technology. There is a need to deploy this opinion mining technology to the recent largest digital activism in Nigeria known as the #EndSARS movement. In this work, we proposed the EndSARS live analytics framework which holds a promising solution to social unrest and may serve as a panacea to curbing the menace of vandalism resulting from unresolved protest issues. Using a dataset of 12,357 tweets, we demonstrated that computational technology can be relevant to addressing online protests. The result of the analysis shows the eight basic emotions expressed during the protest and approaches the government may adopt to address future activisms.

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Big data driven e-government framework in Nigeria

Cited by 3 publications

References 2 publications

A data model for enhanced data comparability across multiple organizations

A data model for enhanced data comparability across multiple organizations

Comparative Analysis of Transformer Models for Sentiment Analysis in Low-Resource Languages

The Sentiment Analysis of EndSARS Protest in Nigeria

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