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Decoding brand sentiments: Leveraging customer reviews for insightful brand perception analysis using natural

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Summary

This study introduces an analytics pipeline combining machine learning and deep learning for smartphone review analysis. It reveals that Convolutional Neural Network (CNN) sentiment analysis paired with Non-Negative Matrix Factorization (NMF) topics offers superior business insights.

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Area of Science:

  • Data Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Traditional feedback methods are insufficient for real-time online reviews.
  • Analyzing customer sentiment is crucial for brand perception and product strategy in competitive markets like smartphones.

Purpose of the Study:

  • To develop an end-to-end analytics pipeline for transforming unstructured online reviews into actionable business insights.
  • To compare the effectiveness of various Machine Learning (ML) and Deep Learning (DL) models for sentiment classification and topic modeling in the smartphone market.

Main Methods:

  • Utilized a dataset of ~68,000 Amazon reviews for ten smartphone brands.
  • Employed class weighting to mitigate class-imbalanced data.
  • Performed sentiment classification using ML (Decision Trees, Logistic Regression, SVM, Naive Bayes) and DL (CNN, RNN, LSTM) models.
  • Extracted underlying themes using Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF).
  • Assessed topic-level sentiment using the Valence Aware Dictionary and Sentiment Reasoner (VADER).
  • Visualized results via an interactive Tableau dashboard.

Main Results:

  • Convolutional Neural Network (CNN) achieved the highest sentiment classification accuracy (85.07%), performing well on positive and negative reviews but struggling with neutral ones.
  • Non-Negative Matrix Factorization (NMF) topic modeling yielded more interpretable topics (Coherence Score=0.54) compared to Latent Dirichlet Allocation (LDA) (Coherence Score=0.41).
  • Linked specific features like battery life and camera quality to customer sentiment.

Conclusions:

  • The integrated pipeline, combining CNN for sentiment analysis and NMF for topic modeling, provides richer, business-ready insights than sentiment analysis alone.
  • Interactive visualization enables practitioners to track sentiment trends, explore topics, and compare brand performance effectively.
  • Acknowledged ethical considerations including potential bias and proposed future work on cross-domain generalization and fairness.