Related Experiment Video
Updated: Jun 25, 2025
03:14
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
544
Scalable deep learning framework for sentiment analysis prediction for online movie reviews
Peter Atandoh1, Fengli Zhang1, Mugahed A Al-Antari2
1School of Information and Software Engineering, University of Electronic Science and Technology of China, North Jianshe Road, Chengdu, 610054, Sichuan, China.
Heliyon
|May 24, 2024
Summary
This study introduces the PEW-MCAB model for sentiment analysis in movie reviews. The deep learning approach achieves high accuracy by analyzing word order and hidden meanings in text.
Area of Science:
- Natural Language Processing
- Deep Learning
Background:
- Sentiment analysis is crucial for e-commerce, especially the online movie industry.
- Understanding word order and hidden meanings in reviews enhances sentiment analysis.
Purpose of the Study:
- To present an enhanced text representation and deep learning model for sentiment analysis.
- To analyze word information order and uncover hidden meanings in online movie reviews.
Main Methods:
- Developed the PEW-MCAB model, utilizing Positional Embedding and pre-trained Glove Embedding Vector (PEW) for text representation.
- Integrated a Multichannel Convolutional Neural Network (MCNN) with an Attention-based Bidirectional Long Short-Term Memory (AB) model.
- Evaluated the model on four datasets: IMDB, MR (2002), MRC (2004), and MR (2005).
Main Results:
- The PEW-MCAB model achieved high accuracy rates: 90.3% (IMDB), 84.1% (MR 2002), 85.9% (MRC 2004), and 87.1% (MR 2005).
- The model effectively categorizes sentiments by treating the full text as a unified piece.
Conclusions:
- The proposed PEW-MCAB model demonstrates significant promise for effective and competitive sentiment analysis in practical applications.
- The enhanced text representation and deep learning architecture contribute to improved performance in analyzing online movie reviews.
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