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Analysis of Evaluated Sentiments; a Pseudo-Linguistic Approach and Online Acceptability Index for Decision-Making with Data: Nigerian Election in View

机译：评价情感分析;数据决策的伪语言方法和在线可接受性指数：尼日利亚大选

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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.
机译：正确测量的情绪总能为将来的事情指明方向。如果没有通过感情加上敏感表述的表达，就很难预测未来的发生。但是，当通过口头语言或书面文字表达情感时，就可以在一定程度上评估对未来事件的预测。尼日利亚拥有丰富的知识分子，超过48％的人口积极参与社交媒体。这个伟大国家的美丽在于其民主的多样性和实践。自独立以来，他们在处理来之不易的民主上经历了种种变化。本文的目的是比较6个地缘政治地区的尼日利亚人的分析情绪以及2019年尼日利亚大选的后果。使用Python编程语言从社交媒体上跨两个主要平台twitter和Facebook检索数据。稍后引入词云，以使用螺旋循环将各种工件映射到语料库来区分各种情感。维达机器学习系统称为情感强度分析器，用于分析每条陈述以检索正面和负面情绪。这项研究采用了两种方法，即定量和定性方法，并在数据分析中使用了大量的描述性方法。研究人员以2019年尼日利亚大选为例，研究了分析结果，以验证未来是否可以根据社交媒体生成的数据做出重大决策。开发了仪表板以绘制不同的感觉以及它们如何影响大选结果。 PHP和JavaScript用于实现此目的。建议“数字人文与艺术”领域的利益相关者探索本文的发现，尤其是当结果至少接近真实结果的80％时。

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