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Related Experiment Video

Updated: Jul 25, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Topic sentiment analysis based on deep neural network using document embedding technique.

Azam Seilsepour1, Reza Ravanmehr1, Ramin Nassiri1

  • 1Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

The Journal of Supercomputing
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning method for domain-specific sentiment analysis (SA). The Embedding Topic Sentiment Analysis using Deep Neural Networks (ETSANet) model improves accuracy by finding semantically related documents for better topic modeling and sentiment classification.

Keywords:
CNNGRUSemantic similaritySemantic topic vectorTopic modelingTopic sentiment analysis

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Sentiment Analysis (SA) is challenging due to domain-specific language, limiting traditional models and lexicons.
  • Sequential Topic Modeling (TM) and SA lack accuracy; simultaneous approaches often fail with domain-specific terms.

Purpose of the Study:

  • To propose a novel supervised hybrid Topic Sentiment Analysis (TSA) approach, ETSANet, for improved domain-specific sentiment analysis.
  • To address limitations of existing methods in accurately capturing domain-specific sentiment polarity.

Main Methods:

  • Developed Semantically Topic-Related Documents Finder (STRDF) to identify contextually relevant training documents using Semantic Topic Vectors.
  • Employed a hybrid CNN-GRU deep neural network trained on semantically related documents.
  • Utilized a hybrid metaheuristic optimization (Grey Wolf + Whale Optimization) for hyperparameter tuning.

Main Results:

  • ETSANet effectively extracts semantic relationships between topics and training data.
  • The hybrid CNN-GRU model achieved superior performance compared to existing methods.
  • Demonstrated a 1.92% increase in accuracy over state-of-the-art techniques.

Conclusions:

  • ETSANet offers a more accurate and robust approach to domain-specific sentiment analysis.
  • The novel STRDF and hybrid deep learning architecture are key to the improved performance.