International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 3 (May-June 2026) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.

Demystifying Consumer Sentiment: An Accessible Approach to Analyzing Earbud Reviews for E-commerce Insights

Author(s) Ms. Satakshi Dwivedi, Prof. Jyoti Bhargava
Country India
Abstract ABSTRACT
Aim: This research seeks to provide an accessible method to sentiment analysis for individuals without any advance computer science backgrounds. To illustrate this, we are conducting an analysis of online product reviews for "Noise" brand earbuds to assess consumer sentiment and satisfaction.

Study Design: This study used a mix of quantitative and qualitative research methods. The data was collected by web scraping all 5,078 customer reviews for every one of the 38 earbud models available on the official "Noise" e-commerce website.
Methodology: The 5,078 reviews were analyzed using Microsoft Excel and Azure Machine Learning to classify each one as either positive, negative, or neutral. The results were then visualized in two ways: a pie chart showed the overall distribution of these sentiments, and a word cloud highlighted the most common words used in the reviews.
Results: An analysis of 5,078 reviews showed that the overwhelming majority were positive. Specifically, 94% (4,756 reviews) were positive, while only a small fraction were negative (2% or 124 reviews) or neutral (4% or 198 reviews). A word cloud was also created to highlight the main themes and keywords from the customer feedback.
Conclusion: The study successfully demonstrated the practicality of using accessible tools for sentiment analysis. The findings offer valuable insights into customer satisfaction with "Noise" earbuds, validating the efficacy of the methodology. Future research could explore diverse datasets and advanced machine learning techniques to expand these findings.
Keywords Customer Reviews, Product Reviews, E-commerce, Sentiment Analysis.
Field Business Administration
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-08-17
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.53714

Share this