Performance Evaluation and Comparison using Deep Learning Techniques in Sentiment Analysis

One of the most common applications of deep learning algorithms is sentiment analysis. This study delivers a better performing and efficient automated feature extraction technique when compared to previous approaches. Traditional methodologies like surface approach will use the complicated manual feature extraction process, which forms the fundamental aspect of feature driven advancements. These methodologies serve as a strong baseline to determine the predictability of the features, and it will also serve as the perfect platform for integrating the deep learning techniques. The proposed research work has introduced a deep learning technique, which can be incorporated with feature-extraction. Moreover, this research work includes three crucial parts. The first step is the development of sentiment classifiers with deep learning, which can be used as the baseline for comparing the performance. This is followed by the use of ensemble techniques and information merger to obtain the final set of sources. As the third step, a combination of ensembles is introduced to categorize various models along with the proposed model. Finally experimental analysis is carried out and the performance is recorded to determine the best model with respect to the deep learning baseline.