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Using back-and-forth translation to create artificial augmented textual data for sentiment analysis models
- Source :
- Expert Systems with Applications. 178:115033
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Sentiment analysis classification models trained using neural networks require large amounts of data, but collecting these datasets requires significant time and resources. Although artificial data has been used successfully in computer vision, there are few effective and generalizable methods for creating artificial augmented text data. In this paper, a text based data augmentation method is proposed called back-and-forth translation that can be used to artificially increase the size of any natural language dataset. By creating augmented text data and adding it to the original dataset, it is demonstrated by empirical experiments that back-and-forth translation data augmentation can reduce the error rate in binary sentiment classification models by up to 3.4%. These results are shown to be statistically significant.
- Subjects :
- 0209 industrial biotechnology
Artificial neural network
Computer science
business.industry
Sentiment analysis
General Engineering
Word error rate
Binary number
02 engineering and technology
Translation (geometry)
computer.software_genre
Computer Science Applications
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Natural language
Natural language processing
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 178
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........3c1fc97c96af5d8c75071aed167d8c46