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Visual Recommendation for Peer-To-Peer Accommodation with Online Reviews based on Sentiment Analysis and Topic

Dong Li¹, Hong Yin¹, Changbo Wang¹

¹East China Normal University, Zhongshan, China.

Journal of Visualization

|June 1, 2022

Summary

This study introduces a pipeline for personalized peer-to-peer accommodation recommendations using user reviews. It analyzes opinions and sentiment to enhance consumer choice and provide business insights.

Keywords:

Deep learning Opinion mining Personalized recommendation Sentiment analysis Visual analysis

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Area of Science:

Artificial Intelligence
Data Science
Consumer Behavior Analysis

Background:

The sharing economy, particularly peer-to-peer (P2P) accommodation, is rapidly growing.
User-generated content (reviews) in P2P accommodation is rich in insights but overwhelming in volume.
Effective analysis of reviews is crucial for consumer decision-making and service improvement.

Purpose of the Study:

To develop a comprehensive pipeline for personalized accommodation recommendations.
To extract actionable insights from user reviews for both consumers and managers.
To create a visual analytic system for interactive exploration of accommodation data.

Main Methods:

Topic modeling to mine opinions from user reviews.
Deep learning for sentiment analysis of reviews.
Aspect-level sentiment analysis to generate sentiment vectors for accommodations.
Development of a personalized recommendation algorithm.
Design of a user-friendly visual analytic system.

Main Results:

A robust pipeline for processing P2P accommodation reviews.
Generation of sentiment vectors representing accommodation characteristics.
A personalized accommodation recommendation system.
A visual analytic system facilitating interactive data exploration.
Demonstrated usefulness and effectiveness through user and case studies.

Conclusions:

The proposed method effectively processes user reviews for personalized recommendations.
The system provides valuable insights for both consumers and P2P accommodation managers.
Visual analytics enhance the understanding and usability of review data.