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Full Description
This book gathers selected papers presented at the International Conference on Sentimental Analysis and Deep Learning (ICSADL 2021), jointly organized by Tribhuvan University, Nepal; Prince of Songkla University, Thailand; and Ejesra during June, 18-19, 2021. The volume discusses state-of-the-art research works on incorporating artificial intelligence models like deep learning techniques for intelligent sentiment analysis applications. Emotions and sentiments are emerging as the most important human factors to understand the prominent user-generated semantics and perceptions from the humongous volume of user-generated data. In this scenario, sentiment analysis emerges as a significant breakthrough technology, which can automatically analyze the human emotions in the data-driven applications. Sentiment analysis gains the ability to sense the existing voluminous unstructured data and delivers a real-time analysis to efficiently automate the business processes. Meanwhile, deep learning emerges as the revolutionary paradigm with its extensive data-driven representation learning architectures. This book discusses all theoretical aspects of sentimental analysis, deep learning and related topics.
Contents
Analysis of Healthcare Industry Using Machine Learning Approach: A Case Study in Bengaluru Region.- Dynamic Document Localization for Ecient Mining.- SentiSeries: A Trilogy of Customer Reviews, Sentiment Analysis and Time Series.- Video Summarization using Fully Convolutional Residual Dense Network.- An Efficient Deep Learning Approach for Detecting Pneumonia Using the Convolutional Neural Network.- QMCDS: Quantum Memory for Cloud Data Storage.- A Study towards Bangla Fake News Detection using Machine Learning and Deep Learning.- A Deep Learning Approach to Analyze the Propagation of Pandemic in America.- Graph Convolution Based Joint Learning of Rumour with Content, User Credibility, Propagation Context and Cognitive as well as Emotion Signals.- Deep Learning based Real Time Object Classification and Recognition using Supervised Learning Approach.
