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Brand public opinion data analysis method based on deep learning.

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This study enhances online public opinion analysis by improving emotion dictionaries for Latent Dirichlet Allocation (LDA) topic models. It uses Bidirectional Encoder Representations from Transformers (BERT) for more accurate sentiment classification of brand A on Weibo.

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Area of Science:

  • Computational Linguistics
  • Social Media Analytics
  • Sentiment Analysis

Background:

  • The rise of internet information and digital technologies amplifies the impact of online public opinion on brand perception and consumer behavior.
  • Effectively managing sudden online public opinion is a critical challenge for brand owners in the digital age.
  • Weibo serves as a significant platform for public discourse and brand-related discussions.

Purpose of the Study:

  • To enhance the accuracy of sentiment analysis in public opinion events by improving emotion dictionaries.
  • To investigate the effectiveness of an enhanced emotion dictionary within the Latent Dirichlet Allocation (LDA) topic model framework.
  • To apply sentiment classification using Bidirectional Encoder Representations from Transformers (BERT) with pre-trained word vectors.

Main Methods:

  • Tracking topic data related to public opinion events for a specific brand (Brand A) on Weibo.
  • Developing an enhanced emotion dictionary using algorithms that leverage topic words and benchmark words, contextualized for Dalian University of Technology.
  • Integrating the improved emotion dictionary with pre-trained word vectors and employing a BERT linear sentiment classification model for analysis.

Main Results:

  • Demonstration of the effectiveness of the enhanced emotion dictionary through integration with BERT.
  • Provision of more accurate sentiment analysis results for online public opinion data.
  • Deeper insights into public opinion trends and sentiment orientation concerning Brand A.

Conclusions:

  • The proposed method of enhancing emotion dictionaries significantly improves sentiment analysis accuracy for online public opinion.
  • Combining LDA topic modeling with an improved emotion dictionary and BERT-based sentiment classification offers a robust approach to understanding public sentiment.
  • The study provides valuable tools and insights for brands to navigate and respond to online public opinion effectively.