Empirical evaluation of deep learning models for sentiment analysis

Abstract

The availability of computing resources and generation of large scale data emanating from Artificial Intelligence, Internet of Things and social media platforms have resulted into resurgence of deep learning technology. Deep learning architectures have been successfully adopted to solve the problems arising in variety of domains such as computer vision, information retrieval, robotics, and natural language processing, etc. Due to inherent ability of deep architectures to extract hierarchical structures from complex multimedia data, they have been widely used for the tasks of classification, regression and prediction. Motivated by the same, this paper addresses the problem of identifying the subjective information from text documents and predicting the sentiments at sentence level using deep feedforward neural network with global average pooling and long short term memory model with dense layers. The experimentation details state that both models are on par and provide good accuracy on the benchmarked dataset of sentiment classification.

Subject Classification:

• (2010) 68M12

Keywords:

• Deep feedforward neural network
• Deep learning
• Long short term memory model
• Sentiment analysis
• Social media analytics