Applications and Enhancement of Document-Based Sentiment Analysis in Deep learning Methods: Systematic Literature Review

Abstract

Sentiment analysis has become a highly effective research field in the natural language domain and has a large scope of real-world implementations. An existing active study concentration for sentiment analysis is the development of graininess at the document level, appearing with two featured objectives: subjectivity classification, which determines whether a document is objective or subjective and sentiment detection which defines whether or not a document has a sentiment. Deep learning approaches have featured as a chance for developing these objectives with their ability to present both syntactic and semantic characteristics of text without demands for high-level attribute engineering. In this paper, we focus to produce a systematic literature review of deep learning methods for document-based sentiment analysis to determine different features in the text. In addition, this systematic literature review presents a brief survey, evaluation, enhancement of recent developments in the field of sentiment analysis techniques and applications of documents for deep learning, starting with the Convolutional Neural Network, continues to cover the Recurrent Neural Network, including Long Short-Term Memory and Gated Repetitive Units. This review also contains the implementation and application of Recursive Neural Network, Deep Belief Network, Domain-Adversarial Network Models and Hybrid Neural Network. This work considers most of the papers published when the history of deep learning began, and specifically the sentiment analysis of the documents.

Keywords

• Deep Learning Models
• Document Level
• Sentiment Analysis
• Applications
• Improvement
• SLR