An Assessment of Machine Learning Applications in Customer Sentiment Analysis: A Study of E-Commerce Businesses in Kwara State
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
- Reference Style:
- Recommended for :
- NGN 5000
Background of the Study
Machine learning (ML) has become a transformative technology in understanding customer sentiment by analyzing textual, visual, and auditory data from various sources. Sentiment analysis enables businesses to gauge customer opinions and improve their products and services accordingly (Jones & Peters, 2024). E-commerce platforms extensively utilize machine learning algorithms to process customer reviews, social media comments, and other data to drive decision-making.
In Kwara State, the e-commerce sector is growing rapidly as businesses embrace digital platforms to reach broader markets. However, many businesses lack advanced tools to effectively analyze customer sentiment, which is crucial for enhancing customer experience and competitiveness (Akinola & Musa, 2025). This study assesses the use of ML applications in customer sentiment analysis among e-commerce businesses in Kwara State.
Statement of the Problem
E-commerce businesses in Kwara State face challenges in understanding customer needs and preferences due to a lack of advanced analytical tools. Manual analysis of customer feedback is time-consuming and prone to errors, leading to missed opportunities for growth and customer satisfaction. Machine learning offers solutions but is often underutilized due to technical and financial constraints (Okoro & Bello, 2023). This study investigates the role of ML in improving customer sentiment analysis for e-commerce businesses in Kwara State.
Objectives of the Study
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To assess the adoption of machine learning applications in customer sentiment analysis among e-commerce businesses in Kwara State.
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To evaluate the effectiveness of machine learning in improving customer sentiment analysis.
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To identify challenges faced by e-commerce businesses in implementing machine learning for sentiment analysis.
Research Questions
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What is the adoption level of machine learning applications in customer sentiment analysis among e-commerce businesses in Kwara State?
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How effective is machine learning in improving customer sentiment analysis?
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What challenges do e-commerce businesses face in implementing machine learning for sentiment analysis?
Research Hypotheses
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There is no significant relationship between the adoption of machine learning and improvements in customer sentiment analysis.
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Machine learning does not significantly enhance customer feedback analysis in e-commerce businesses.
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The challenges in implementing machine learning for sentiment analysis are not significant in e-commerce businesses in Kwara State.
Scope and Limitations of the Study
The study focuses on e-commerce businesses in Kwara State, evaluating their use of machine learning for customer sentiment analysis. Limitations include access to customer feedback data and the varying levels of ML adoption across businesses.
Definitions of Terms
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Machine Learning (ML): A subset of artificial intelligence that uses algorithms to learn patterns from data and make predictions.
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Sentiment Analysis: The process of identifying and categorizing opinions expressed in text to determine the sentiment of the author.
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E-Commerce Businesses: Companies that conduct commercial transactions online.
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