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Data-Driven Contextual Valence Shifter Quantification for Multi-Theme Sentiment Analysis.

Hongkun Yu1, Jingbo Shang1, Meichun Hsu2

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Summary
This summary is machine-generated.

This study introduces a new framework for analyzing sentiment in online reviews, accurately predicting word sentiment across different themes and identifying contextual sentiment shifters.

Keywords:
Multi-ThemeSentiment AnalysisSentiment Shifting

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Sentiment Analysis

Background:

  • Traditional sentiment analysis struggles with context-dependent word polarity across diverse themes.
  • Sentiment polarity can shift based on thematic context and valence shifters, posing challenges for accurate analysis.

Purpose of the Study:

  • To develop a data-driven framework for multi-theme sentiment analysis at the word level.
  • To enable accurate polarity predictions for words across different review themes.
  • To discover and quantify the impact of contextual valence shifters on sentiment.

Main Methods:

  • Factorizing review sentiments using theme/word embeddings to model multi-theme sentiment.
  • Formulating the contextual valence shifter effect as a logistic regression problem.
  • Developing a data-driven framework to address multi-theme and sentiment shifting challenges.

Main Results:

  • Significant improvement in sentiment polarity classification accuracy.
  • Demonstrated effectiveness of the framework in handling multi-theme and sentiment shifting phenomena.
  • Successful multi-theme word sentiment predictions and automatic quantification of shifter effects.

Conclusions:

  • The proposed framework effectively addresses the complexities of multi-theme and sentiment shifting in review analysis.
  • The study highlights the importance of considering thematic context and valence shifters for accurate sentiment analysis.
  • The framework offers a robust solution for nuanced sentiment understanding in user-generated content.