Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU - Raza, Muhammad Raheel; Hussain, Walayat; Merigo, Jose Maria (Institute of Electrical and Electronics Engineers Inc., 2021)
Cloud computing has become a de facto choice of many individuals and enterprises for computing solutions. In the last few years, many cloud providers appear in the market that offers the same services. It is a trivial job to choose an optimal service best suited for organisations in such a massive arms race of service providers. Existing consumer experience could help significantly build a holistic perception of their experiences that ultimately influence service adoption decisions. Sentiment analysis is an effective tool to understand consumer experience about the product or service. The sophisticated sentiment analysis could help businesses to gain a better insight and respond proactively to consumer issues. There are various methods for sentiment analysis that produces ideal results under different conditions. Therefore, it is very important to choose the right method to predict consumer's sentiment for a greatest result. In this paper we analyse the sentiment prediction accuracy of widely used neural network methods - recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent network (GRU). We use software as a service (SaaS) dataset having 6258 reviews. From analysis results we find that GRU outperforms the LSTM and RNN methods. © 2021 IEEE.
2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021 -- 6 October 2021 through 8 October 2021 -- Elazig -- 174400
Keywords: Cloud reviews; Forecasting; GRU; LSTM; RNN; Sentiment prediction; Social influence