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A decision-making algorithm combining the aspect-based sentiment analysis and intuitionistic fuzzy-VIKOR for online
Zaoli Yang1, Yue Gao2, Xiangling Fu2
1College of Economics and Management, Beijing University of Technology, Beijing, 100124 China.
Annals of Operations Research
|November 8, 2021
Summary
This study introduces a novel model using bidirectional long short-term memory (Bi-LSTM) with an attention mechanism to analyze hotel reviews, improving decision-making for online travel bookings.
Area of Science:
- Natural Language Processing
- Data Mining
- Artificial Intelligence
Background:
- Online reviews are crucial for hotel selection but overwhelming for customers.
- Manual review analysis is time-consuming and impractical due to large volumes.
Purpose of the Study:
- To develop an automated system for extracting product attributes from online reviews.
- To enhance the hotel reservation process by providing data-driven insights.
- To improve decision-making for customers in online travel platforms.
Main Methods:
- Proposed a model combining bidirectional long short-term memory (Bi-LSTM) and an attention mechanism for aspect-based sentiment analysis.
- Applied the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method for multi-attribute decision-making.
- Crawled and analyzed online hotel reviews from three major travel platforms.
Main Results:
- The Bi-LSTM model with attention significantly improved prediction accuracy compared to a single LSTM model.
- The system successfully extracted multiple product attributes from review texts.
- A statistical matrix was generated for decision-making support.
Conclusions:
- The proposed Bi-LSTM with attention mechanism offers an effective solution for analyzing online reviews.
- The integration with VIKOR provides a robust method for ranking hotels based on multiple attributes.
- This approach aids customers in making more informed hotel reservation decisions.
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