An Intelligent Opinion Mining System with the Assistance of Bi-Directional Deep Recurrent Neural Network for Sentiment Analysis

Abstract

Opinion mining, also referred to as sentiment analysis, has been an important research topic for identifying and analyzing opinions in natural language text. Computational linguistics and information retrieval are combined to extract and assess subjective information from textual data in this interdisciplinary field. In this work, I present a Bidirectional Deep Recurrent Neural Network (BDRNN) based framework for sentiment polarity detection. In this work, we propose the Sentiment Analysis using BDRNN (SA-BDRNN) system, which aims to overcome the challenges in extracting unbiased opinions from text corpora. A dataset of labeled sentiments is used for training and evaluation to assure good model performance. Then the framework aims to mine sentiments effectively from source documents and improve the understanding of important contextual information. Finally, the SA-BDRNN system is benchmarked against state of the art methods, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and an optimized SVM using Particle Swarm Optimization (SVM-PSO). Experimental results show that the proposed SA-BDRNN framework outperforms the best previous solutions in terms of sentiment classification accuracy and robustness, and hence can be a promising solution to advanced opinion mining applications.