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E-Commerce Brand Ranking Algorithm Based on User Evaluation and Sentiment Analysis.

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This study introduces a new algorithm to rank e-commerce product influence by analyzing user reviews and sentiment. The method enhances accuracy and efficiency in identifying opinion leaders and improving brand rankings.

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Area of Science:

  • E-commerce and Consumer Behavior
  • Data Science and Machine Learning
  • Social Network Analysis

Background:

  • Consumers face challenges comparing products across merchants.
  • Understanding user needs and product advantages is crucial for e-commerce success.
  • Quantifying sentiment in reviews and identifying influential users are key research areas.

Purpose of the Study:

  • To develop a method for quantifying sentiment in product reviews.
  • To identify influential opinion leaders in e-commerce platforms.
  • To improve product recommendation and e-commerce brand ranking accuracy.

Main Methods:

  • Utilized Python web crawling (BeautifulSoup) for efficient data acquisition.
  • Implemented data cleaning, deduplication, and preprocessing including semantic analysis.
  • Proposed the SRank algorithm, integrating node similarity and LeaderRank, to analyze network influence.

Main Results:

  • Calculated sentiment polarity of user comments to determine user influence ranking.
  • Identified opinion leaders more objectively by integrating user activity and comment sentiment.
  • Demonstrated improved operational efficiency and accuracy in e-commerce brand ranking compared to PageRank and SRank.

Conclusions:

  • The proposed opinion leader identification model provides more reasonable and objective rankings.
  • Successfully integrated sentiment analysis into social network analysis for e-commerce.
  • The research enhances the accuracy of e-commerce brand rankings and user influence identification.