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Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices.

Keshav Kaushik1, Akashdeep Bhardwaj1, Susheela Dahiya1

  • 1School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India.

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Summary
This summary is machine-generated.

This study introduces a deep learning model for accurate customer sentiment analysis, achieving 93% accuracy in predicting satisfaction with products like Amazon Alexa. This artificial intelligence approach helps businesses understand consumer feedback effectively.

Keywords:
alexaamazonartificial intelligenceinternet of thingsmachine learningnatural language processingsmart devices

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Natural Language Processing

Background:

  • Businesses require accurate methods to gauge consumer satisfaction with products and services.
  • Analyzing customer reviews is crucial for understanding market perception and improving offerings.
  • Existing sentiment analysis tools may lack the precision needed for nuanced feedback interpretation.

Purpose of the Study:

  • To develop and evaluate a deep learning model for automatic customer sentiment analysis.
  • To accurately categorize user reviews of Amazon Alexa into positive or negative sentiments.
  • To provide a scalable solution for companies to analyze online customer feedback.

Main Methods:

  • A dataset of 3150 Amazon Alexa user reviews was collected and preprocessed.
  • A deep learning model, specifically a multinomial naive Bayesian classifier, was implemented.
  • The model was trained on 80% of the dataset and validated on the remaining 20%.

Main Results:

  • The deep learning model achieved a high accuracy of 93% in sentiment classification.
  • Initial analysis included plotting word clouds to visualize sentiment distribution.
  • The proposed model outperformed three out of four benchmark models in the same domain.

Conclusions:

  • The developed deep learning model offers a highly accurate and effective method for customer sentiment analysis.
  • This approach can be readily adopted by any business with an online presence to automate review analysis.
  • The research demonstrates the power of artificial intelligence and machine learning in understanding consumer feedback.