Word-Embedding-Based Traffic Document Classification Model for Detecting Emerging Risks Using Sentiment Similarity Weight.
Min-Jeong KimJi-Soo KangKyungyong ChungPublished in: IEEE Access (2020)
Keyphrases
- document similarity
- sentence level
- word similarity
- document space
- sentiment analysis
- document level
- cosine similarity
- similarity measure
- related words
- vector space
- compound words
- co occurrence
- information retrieval
- word co occurrence
- keywords
- concept space
- latent topics
- sentiment classification
- word level
- semantic similarity
- term frequency
- text corpus
- document clustering
- multidimensional scaling
- spoken document retrieval
- document collections
- tf idf
- short list
- sentence similarity
- document images
- related documents
- network traffic
- word pairs
- information retrieval systems
- semantic information
- document frequency
- text documents
- distance function
- noun phrases
- opinion mining
- distance measure
- word sense
- word sense disambiguation
- text categorization
- document representation
- opinion retrieval
- binary codes
- term weighting
- document retrieval
- n gram
- wordnet
- relevant documents
- text mining
- retrieval systems
- risk management
- traffic flow
- translation model