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Quantifying customer sentiment for automobile brand perception analysis using machine learning on Twitter.

Sujith Samuel Mathew1, Kadhim Hayawi2, Neethu Venugopal1

  • 1College of Interdisciplinary Studies, Zayed University, Abu Dhabi, United Arab Emirates.

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This study introduces a Brand Polarity Score (BPS) using sentiment analysis of Twitter data to track customer perceptions of automobile brands. The BPS offers real-time insights into brand positioning and sentiment dynamics.

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Area of Science:

  • Social media analytics
  • Brand management
  • Natural Language Processing

Background:

  • Social networking sites are vital for public opinion and brand perception.
  • Brand managers utilize these platforms for consumer insights.
  • Understanding customer sentiment is crucial for brand strategy.

Purpose of the Study:

  • To develop a quantitative measure of customer sentiment towards automobile brands.
  • To assess brand perception using sentiment analysis on Twitter data.
  • To introduce a weighted 'Brand Polarity Score' (BPS) for dynamic brand monitoring.

Main Methods:

  • Sentiment analysis applied to Twitter (or X) data for five leading automobile brands.
  • Development of the Brand Polarity Score (BPS) model.
  • Weighting the BPS by tweet influence (engagement metrics, author's follower count).

Main Results:

  • The BPS effectively quantifies customer sentiment (positive/negative) towards brands.
  • The score provides near real-time brand positioning and sentiment tracking.
  • Validation confirmed the robustness of the BPS system through various assessments.

Conclusions:

  • The proposed Brand Polarity Score (BPS) is a valuable tool for monitoring brand perception.
  • BPS facilitates progressive and competitive brand analyses.
  • This system contributes to a comprehensive understanding of brand dynamics in the digital age.