Long short-term memory-based sentiment classification of cloud dataset
Raza, Muhammad Raheel Hussain, Walayat Merigo, Jose
Raza, Muhammad Raheel
Hussain, Walayat
Merigo, Jose
Abstract
Text Sentiment Classification is a crucial task for various decision-making processes in many organizations. It identifies the polarity of texts positively and negatively and highlights the opinions and views hidden within the comments or reviews of a product or service. Performing it on big data from social media and related sources is quite tricky and time-consuming. Nowadays, Deep Learning (DL) is widely used for sentiment analysis due to its high performance. In this paper, Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) approach is applied to perform sentiment analysis of a cloud review dataset. The cloud review dataset contains cloud consumer reviews regarding the services provided by different cloud service providers and the dataset is achieved as a result of the Harvesting-as-a-Service (HaaS) framework. The study focuses on observing the behaviour of the deep learning RNN-LSTM approach on a cloud dataset. Results of the experiment are evaluated using various evaluation and performance metrics. The approach tends to achieve 95 % accuracy.
Keywords
Measurement, Deep learning, Sentiment analysis, Technological innovation, Recurrent neural networks, Social networking (online), Computational modeling
Date
2021
Type
Conference paper
Journal
Book
Volume
Issue
Page Range
1-6
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business