Sentiments measured properly always give direction to future occurrences. Without an expression through feelings plus sensitive statements, it would be difficult to predict future occurrence. But when feelings are expressed through spoken languages or written texts, a projection of future event can be evaluated to an extent. Nigeria is blessed with intellectuals and over 48% of the population are actively involved in social media. The beauty of this great nation is in its diversity and practice of democracy. Since independence, they have experienced variations in handling their hard-earned democracy. The goal of this paper is to compare analyzed sentiments from the Nigerian people across the 6 geopolitical zones and the aftermath of the Nigerian election in 2019. Data is retrieved from the social media using python programming language across 2 major platforms twitter and Facebook. A word cloud is introduced later to differentiate various sentiments using a spiral loop to map the various artifacts into corpora. Vader machine learning system called Sentiment Intensity Analyzer was used to the analyze each statement to retrieve positive and negative sentiments. This study employs two methodologies, quantitative and qualitative methods with significant levels of descriptive approach in data analysis. The researchers explore the results of the analysis to verify whether significant decisions can be made in the future from data generated from social media, using the 2019 Nigerian election as a case study. A dashboard was developed to plot the different feelings and how they influenced the general election outcome. PHP and JavaScript were used to achieve this. It is recommended that stakeholders in the ‘digital humanities and arts’ explore the findings in this paper especially if the result comes at least close to 80% of the real result.
| Published in | Internet of Things and Cloud Computing (Volume 7, Issue 2) |
| DOI | 10.11648/j.iotcc.20190702.11 |
| Page(s) | 39-44 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
Sentiment Analysis, Social Media, Machine Learning, Prediction, Nigerian Election
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APA Style
Okpala Izunna Udebuana, Ijioma Patricia Ngozi, Emejulu Augustine Obiajulu. (2019). Analysis of Evaluated Sentiments; a Pseudo-Linguistic Approach and Online Acceptability Index for Decision-Making with Data: Nigerian Election in View. Internet of Things and Cloud Computing, 7(2), 39-44. https://doi.org/10.11648/j.iotcc.20190702.11
ACS Style
Okpala Izunna Udebuana; Ijioma Patricia Ngozi; Emejulu Augustine Obiajulu. Analysis of Evaluated Sentiments; a Pseudo-Linguistic Approach and Online Acceptability Index for Decision-Making with Data: Nigerian Election in View. Internet Things Cloud Comput. 2019, 7(2), 39-44. doi: 10.11648/j.iotcc.20190702.11
AMA Style
Okpala Izunna Udebuana, Ijioma Patricia Ngozi, Emejulu Augustine Obiajulu. Analysis of Evaluated Sentiments; a Pseudo-Linguistic Approach and Online Acceptability Index for Decision-Making with Data: Nigerian Election in View. Internet Things Cloud Comput. 2019;7(2):39-44. doi: 10.11648/j.iotcc.20190702.11
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Download@article{10.11648/j.iotcc.20190702.11,
author = {Okpala Izunna Udebuana and Ijioma Patricia Ngozi and Emejulu Augustine Obiajulu},
title = {Analysis of Evaluated Sentiments; a Pseudo-Linguistic Approach and Online Acceptability Index for Decision-Making with Data: Nigerian Election in View},
journal = {Internet of Things and Cloud Computing},
volume = {7},
number = {2},
pages = {39-44},
doi = {10.11648/j.iotcc.20190702.11},
url = {https://doi.org/10.11648/j.iotcc.20190702.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20190702.11},
abstract = {Sentiments measured properly always give direction to future occurrences. Without an expression through feelings plus sensitive statements, it would be difficult to predict future occurrence. But when feelings are expressed through spoken languages or written texts, a projection of future event can be evaluated to an extent. Nigeria is blessed with intellectuals and over 48% of the population are actively involved in social media. The beauty of this great nation is in its diversity and practice of democracy. Since independence, they have experienced variations in handling their hard-earned democracy. The goal of this paper is to compare analyzed sentiments from the Nigerian people across the 6 geopolitical zones and the aftermath of the Nigerian election in 2019. Data is retrieved from the social media using python programming language across 2 major platforms twitter and Facebook. A word cloud is introduced later to differentiate various sentiments using a spiral loop to map the various artifacts into corpora. Vader machine learning system called Sentiment Intensity Analyzer was used to the analyze each statement to retrieve positive and negative sentiments. This study employs two methodologies, quantitative and qualitative methods with significant levels of descriptive approach in data analysis. The researchers explore the results of the analysis to verify whether significant decisions can be made in the future from data generated from social media, using the 2019 Nigerian election as a case study. A dashboard was developed to plot the different feelings and how they influenced the general election outcome. PHP and JavaScript were used to achieve this. It is recommended that stakeholders in the ‘digital humanities and arts’ explore the findings in this paper especially if the result comes at least close to 80% of the real result.},
year = {2019}
}
TY - JOUR T1 - Analysis of Evaluated Sentiments; a Pseudo-Linguistic Approach and Online Acceptability Index for Decision-Making with Data: Nigerian Election in View AU - Okpala Izunna Udebuana AU - Ijioma Patricia Ngozi AU - Emejulu Augustine Obiajulu Y1 - 2019/09/09 PY - 2019 N1 - https://doi.org/10.11648/j.iotcc.20190702.11 DO - 10.11648/j.iotcc.20190702.11 T2 - Internet of Things and Cloud Computing JF - Internet of Things and Cloud Computing JO - Internet of Things and Cloud Computing SP - 39 EP - 44 PB - Science Publishing Group SN - 2376-7731 UR - https://doi.org/10.11648/j.iotcc.20190702.11 AB - Sentiments measured properly always give direction to future occurrences. Without an expression through feelings plus sensitive statements, it would be difficult to predict future occurrence. But when feelings are expressed through spoken languages or written texts, a projection of future event can be evaluated to an extent. Nigeria is blessed with intellectuals and over 48% of the population are actively involved in social media. The beauty of this great nation is in its diversity and practice of democracy. Since independence, they have experienced variations in handling their hard-earned democracy. The goal of this paper is to compare analyzed sentiments from the Nigerian people across the 6 geopolitical zones and the aftermath of the Nigerian election in 2019. Data is retrieved from the social media using python programming language across 2 major platforms twitter and Facebook. A word cloud is introduced later to differentiate various sentiments using a spiral loop to map the various artifacts into corpora. Vader machine learning system called Sentiment Intensity Analyzer was used to the analyze each statement to retrieve positive and negative sentiments. This study employs two methodologies, quantitative and qualitative methods with significant levels of descriptive approach in data analysis. The researchers explore the results of the analysis to verify whether significant decisions can be made in the future from data generated from social media, using the 2019 Nigerian election as a case study. A dashboard was developed to plot the different feelings and how they influenced the general election outcome. PHP and JavaScript were used to achieve this. It is recommended that stakeholders in the ‘digital humanities and arts’ explore the findings in this paper especially if the result comes at least close to 80% of the real result. VL - 7 IS - 2 ER -
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