Integration of deep learning techniques for sentiment and emotion analysis of social media data
H.S. Hota, Dinesh Kumar Sharma, Nilesh Verma · International Journal of Intelligent Systems Technologies and Applications · 2023
Sentiment analysis (SA) and emotion analysis (EA) are commonly used to understand people's feelings and opinions on a given topic. COVID-19 is an emerging infectious disease that is rapidly spreading around the world. The mental state of a country's population is more or less the same worldwide. Machine learning (ML) techniques are commonly utilised to analyse human sentiments and emotions. Two popular deep learning (DL) techniques: convolutional neural network (CNN) and long short-term memory (LSTM) are being applied in several areas. In this study, we propose a hybrid of CNN and LSTM to improve the performance of the classification model. The two different models, the sentiment analysis model (SAM) and the emotional analysis model (EAM), were developed using benchmark data, which produces 91.11% and 89.39% accuracy, respectively, by integrating CNN and LSTM. Integration of two or more techniques significantly improves performance by utilising both techniques. The results of the experiments demonstrate that the proposed hybrid technique outperforms other individual DL techniques.