Sentiment Analysis of Nigerian Social Media Posts on Government Policies Using Natural Language Processing

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

Social media platforms have become powerful tools for public expression and engagement, especially in Nigeria where citizens frequently use platforms like X (formerly Twitter), Facebook, and Instagram to discuss government activities. Understanding public opinion through these platforms can help policymakers gauge citizen satisfaction and identify areas needing improvement (Ibrahim, 2021).

Natural Language Processing (NLP) and sentiment analysis provide effective ways to extract and interpret opinions from textual data. Sentiment analysis classifies text as positive, negative, or neutral, thereby providing valuable insights into public attitudes (Gupta & Kumar, 2022). By applying NLP techniques to Nigerian social media data, researchers can monitor how citizens react to policies, decisions, or public service initiatives.

This study explores the use of sentiment analysis to analyze Nigerian social media posts and evaluate public reactions toward government policies.

1.2 Statement of the Problem

Government agencies in Nigeria often lack real-time feedback mechanisms to understand public sentiment about policies. Traditional opinion polls are slow, expensive, and sometimes biased (Okoro, 2020). Consequently, decisions are made without sufficient insight into citizens’ perspectives.

There is a need for an automated system that can continuously analyze social media content and summarize public opinion accurately. Sentiment analysis, powered by NLP, offers a cost-effective solution to this problem.

1.3 Aim and Objectives of the Study

The main aim of this study is to analyze the sentiments of Nigerian social media users toward government policies using NLP techniques.

The specific objectives are to:

1. Collect and preprocess Nigerian social media data related to selected government policies.

2. Develop a sentiment classification model using machine learning techniques.

3. Evaluate the accuracy and performance of the sentiment analysis model.

4. Visualize the sentiment trends for better interpretation and decision-making.

1.4 Research Questions

1. How can NLP techniques be applied to analyze sentiments in Nigerian social media data?

2. What machine learning algorithms provide the most accurate sentiment classification results?

3. How can the results of sentiment analysis improve public policy decisions?

4. What are the major challenges of applying NLP in analyzing social media text from Nigeria?

1.5 Significance of the Study

This research provides an innovative approach to understanding public opinion through social media analytics. It helps policymakers, researchers, and journalists assess how citizens perceive government actions. Furthermore, the study promotes the use of data science for evidence-based governance and improves transparency in public communication (Eze, 2023).

1.6 Scope of the Study

The study focuses on analyzing posts from Nigerian users on major social media platforms related to selected government policies between 2023 and 2025. Data will be collected using publicly available APIs and processed using NLP-based sentiment analysis methods.

1.7 Definition of Terms

Sentiment Analysis: The process of identifying and categorizing opinions expressed in text.
Natural Language Processing (NLP): A technique used in AI to interpret and analyze human language.
Social Media Analytics: The practice of collecting and analyzing data from social platforms to gain insights.