Abstract
By the development of social media, sentiment analysis has changed to one of the most remarkable research topics in the field of natural language processing which tries to dig information from textual data containing users’ opinions or attitudes toward a particular topic. In this regard, deep neural networks have emerged as promising techniques that have been extensively used for this aim in recent years and obtained significant results. Considering the fact that deep neural networks can automatically extract features from data, it can be claimed that intermediate representations extracted from these networks can be also used as appropriate features. While different deep neural networks are able to extract various types of features due to their distinct structures, we decided to combine features extracted from heterogeneous neural networks using multi-view classifiers to enhance the overall performance of document-level sentiment analysis by considering the correlation between them. The proposed multi-view deep network makes use of intermediate features extracted from convolutional and recursive neural networks to perform classification. Based on the results of the experiments, the proposed multi-view deep network not only outperforms single-view deep neural networks but also has superior efficiency and generalization performance.
Highlights
By the rapid development of the World Wide Web, especially social media, a large amount of textual data containing people’s opinions and feelings is generated
Various deep learning methods like convolutional neural network, recurrent neural network, and recursive neural network have been presented since the last previous decades and recent studies are increasingly focusing on their new use and improvement [8], [9]
We report the effect of the filter size, number of filters, hidden state dimension, and dropout rate on one of the variations of the proposed model (CNN-Recurrent Neural Network (RNN)+SMVMED)
Summary
By the rapid development of the World Wide Web, especially social media, a large amount of textual data containing people’s opinions and feelings is generated While this textual data is precious, useful and can be employed by companies, government, and other people for making decisions, there is a need to develop an intelligent system that can automatically extract valuable information from them and classify them based on their polarities. Deep learning methods are able to utilize multiple processing layers to generate various valuable features from data without human intervention [3], [4] These methods have made a dramatic improvement and impressive advancement in different fields, such as computer vision [5], speech recognition [6], and natural language processing [7]. Various deep learning methods like convolutional neural network, recurrent neural network, and recursive neural network have been presented since the last previous decades and recent studies are increasingly focusing on their new use and improvement [8], [9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
