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Text sentiment analysis based on CBOW model and deep learning in big data environment
- Source :
- Journal of Ambient Intelligence and Humanized Computing. 11:451-458
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- For the issues that the accurate and rapid sentiment analysis of comment texts in the network big data environment, a text sentiment analysis method combining Bag of Words (CBOW) language model and deep learning is proposed. First, a vector representation of text is constructed by a CBOW language model based on feedforward neural networks. Then, the Convolutional Neural Network (CNN) is trained through the labeled training set to capture the semantic features of the text. Finally, the Dropout strategy is introduced in the Softmax classifier of traditional CNN, which can effectively prevent the model from over-fitting and has better classification ability. Experimental results on COAE2014 and IMDB datasets show that this method can accurately determine the emotional category of the text and is robust, the accuracy on the two datasets reached 90.5% and 87.2%, respectively.
- Subjects :
- Training set
General Computer Science
business.industry
Computer science
Deep learning
010102 general mathematics
Sentiment analysis
02 engineering and technology
Machine learning
computer.software_genre
01 natural sciences
Convolutional neural network
Bag-of-words model
Softmax function
0202 electrical engineering, electronic engineering, information engineering
Feedforward neural network
020201 artificial intelligence & image processing
Artificial intelligence
Language model
0101 mathematics
business
computer
Dropout (neural networks)
Subjects
Details
- ISSN :
- 18685145 and 18685137
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Journal of Ambient Intelligence and Humanized Computing
- Accession number :
- edsair.doi...........d2afc92df63aa1761317820b94b57358