Word-Embedding-Based Traffic Document Classification Model for Detecting Emerging Risks Using Sentiment Similarity Weight

Word-Embedding-Based Traffic Document Classification Model for Detecting Emerging Risks Using Sentiment Similarity Weight

With the increase in traffic accident rates, traffic risk detection is becoming increasingly important. Moreover, it is necessary to provide appropriate traffic information considering user locations and routes and design an analysis method accordingly. This paper proposes a word-embedding-based tra...

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Bibliographic Details
Main Authors: Min-Jeong Kim, Ji-Soo Kang, Kyungyong Chung
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Classification
risk detection
sentiment analysis
text mining
traffic
word embedding
Online Access:https://ieeexplore.ieee.org/document/9204966/

Internet

https://ieeexplore.ieee.org/document/9204966/

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