# A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews
## 6 Conclusions  
Over the years, several approaches have been developed for sentiment analysis of social media data. This sentiment analysis process is usually complex and time-consuming due to the huge amount of data and the requirement to achieve a high level of accuracy. Thus, this paper presents a deep learning approach for sentiment analysis of Twitter data on COVID-19 reviews. The algorithm is based on an LSTM-RNN-based network and enhanced featured weighting by an attention layer. This algorithm uses an enhanced feature transformation framework via the attention mechanism. A total of four class labels (sad, joy, fear, and anger) from publicly available Twitter data posted in the Kaggle database were used in this study. In comparison with current approaches, the proposed deep learning approach significantly improved the performance metrics, with increases of 20% in accuracy and 10% to 12% in precision but only 12-13% in recall. Out of a total of 179,108 COVID-19-related tweets, tweets with positive, neutral, and negative sentiments were found to account for 45%, 30%, and 25%, respectively. Overall, the proposed deep learning approach is found to be efficient and practical and can be easily implemented for sentiment classification of COVID-19 reviews.  
This study provides theoretical and practical implications. For theoretical implications, this study applies a deep learning approach for sentiment analysis of individuals from Twitter data on information regarding COVID-19. This deep learning approach can be further applied to sentiment analysis of a general decision-making problem in various industries such as marketing, government, service and academic. The implication of this study is evident also from the recent COVID-19 situations when information expressed over the social media have affected public sentiments [44]. For such practical implications, this study suggests that the proposed deep learning approach can be adopted and modified for achieving a good level of accuracy especially when considering the complexities entailed in textual analysis.  
This study is not free from limitations. Feature weighting and feature mapping were applied to the original dataset, and other features that are noisy, as well as a combination of these factors, may affect the classification outcomes. In future work, the deep learning approach can be designed to optimise the features in an iterative process. It can also be enhanced to work so that topic detection and sentiment classification are performed simultaneously.  
Conceptualization, all authors; methodology, C.S.; software, C.S.; validation, C.S.; formal analysis, C.S.; investigation, C.S.; resources, C.S; data curation, C.S.; writing--original draft preparation, C.S.; writing--review and editing, all authors; visualization, C.S.; supervision, T.I., S.W. and S.G.; project administration, C.S.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data available Statement: Data used in this research is available from the Kaggle ([[https://www.kaggle.com/gpreda/covid19-tweets](https://www.kaggle.com/gpreda/covid19-tweets)]([https://www.kaggle.com/gpreda/covid19-tweets](https://www.kaggle.com/gpreda/covid19-tweets)), accessed on 8 January 2022). The authors declare no conflict of interest.