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Big Data Recommendation Research Based on Travel Consumer Sentiment Analysis.

Zhu Yuan1

  • 1School of Business, Jilin Business and Technology College, ChangChun, China.

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|March 17, 2022
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Summary

Analyzing online travel reviews using big data and machine learning reveals valuable consumer sentiment. An improved Support Vector Machine (SVM) algorithm on a Hadoop Distributed File System (HDFS) enables accurate and efficient sentiment classification for travel recommendations.

Keywords:
Map-Reducebig data analysissentiment analysissupport vector machinetourism consumption

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Area of Science:

  • Data Science
  • Artificial Intelligence
  • Tourism Big Data Analytics

Background:

  • The proliferation of online travel reviews generates vast amounts of tourism big data.
  • Analyzing tourist emotions and experiences from online reviews offers significant insights.
  • Existing methods may lack the efficiency and accuracy needed for large-scale sentiment analysis.

Purpose of the Study:

  • To leverage big data and machine learning for analyzing the value of online travel reviews.
  • To propose an improved Support Vector Machine (SVM) algorithm for travel consumer sentiment analysis.
  • To build a Hadoop Distributed File System (HDFS) based on the Map-Reduce model for efficient processing.

Main Methods:

  • Pre-processing of internet travel reviews for sentiment analysis.
  • Development of an improved SVM algorithm focusing on linear classification and kernel functions for enhanced sentiment word classification accuracy.
  • Implementation of a Hadoop platform with HDFS data nodes and Map-Reduce programming model for parallel processing.

Main Results:

  • The proposed improved SVM algorithm demonstrates increased accuracy in sentiment word classification.
  • The Hadoop-based Map-Reduce system significantly reduces processing time through parallelization.
  • The integrated approach effectively classifies travel sentiment from online reviews.

Conclusions:

  • Online travel reviews are a crucial data source for developing travel big data recommendation systems.
  • The implemented method provides a fast and accurate approach to travel sentiment classification.
  • This research validates the utility of advanced machine learning and big data techniques in the tourism domain.