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Emotion computing and Word Mover's Distance
- Source :
- PLoS ONE, PLoS ONE, Vol 13, Iss 4, p e0194136 (2018)
- Publication Year :
- 2018
- Publisher :
- PLOS, 2018.
-
Abstract
- In this paper, we propose an emotion separated method(SeTF·IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF·IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. Inspired by the enormous improvement of the visualization map propelled by the changed distances among the sentences, we being the first group utilizes the Word Mover's Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best f1 scores and have a greater increase compared with the same dimension feature vectors obtained by dimension reduction TF·IDF method. Compared experiments in English corpus also show the efficiency of WMD features in the cross-language field.
- Subjects :
- Computer science
Emotions
lcsh:Medicine
Social Sciences
02 engineering and technology
Anxiety
computer.software_genre
Infographics
Machine Learning
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
Psychology
lcsh:Science
Language
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Semantics
Physical Sciences
020201 artificial intelligence & image processing
Graphs
Natural language processing
Word (computer architecture)
Algorithms
Research Article
Computer and Information Sciences
Feature vector
Emotion classification
Research and Analysis Methods
Machine Learning Algorithms
Dimension (vector space)
Artificial Intelligence
Humans
Computer Simulation
business.industry
Dimensionality reduction
Data Visualization
lcsh:R
Cognitive Psychology
Biology and Life Sciences
020207 software engineering
Linguistics
Models, Theoretical
Visualization
Cognitive Science
lcsh:Q
Artificial intelligence
business
computer
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....af7ae9e920cc35c9af6f456fda3b5912