Computational Intelligence Applications for Text and Sentiment Data Analysis

Computational Intelligence Applications for Text and Sentiment Data Analysis explores the most recent advances in text information processing and data analysis technologies, specifically focusing on sentiment analysis from multifaceted data. The book investigates a wide range of challenges involved in the accurate analysis of online sentiments, including how to i) identify subjective information from text, i.e., exclusion of 'neutral' or 'factual' comments that do not carry sentiment information, ii) identify sentiment polarity, and iii) domain dependency. Spam and fake news detection, short abbreviation, sarcasm, word negation, and a lot of word ambiguity are also explored.Further chapters look at the difficult process of extracting sentiment from different multimodal information (audio, video and text), semantic concepts. In each chapter, the book's authors explore how computational intelligence (CI) techniques, such as deep learning, convolutional neural network, fuzzy and rough set, global optimizers, and hybrid machine learning techniques play an important role in solving the inherent problems of sentiment analysis applications. Introduces recent computational intelligence approaches to text data processing and modeling Surveys the most recent developments and challenges of multimodal data processing and sentiment analysis Presents case studies which implement different algorithms to identify sentiment polarity and domain dependency INDICE: 1. Introduction to Text and Sentiment Data Analysis2. Natural Language Processing and Sentiment Analysis: Perspectives from Computational Intelligence3. Applications and Challenges of Sentiment Analysis in Real Life Scenarios4. Emotions Recognition of Students from Online and Offline Texts5. Online Social Network Sensing Models6. Identifying Sentiments of Hate Speech using Deep Learning7. An Annotation System to Summarize Medical Corpus using Sentiment based Models8. Deep learning-based Dataset Recommendation System by employing Emotions9. Hybrid Deep Learning Architecture Performance on Large English Sentiment Text Data: Merits and Challenges10. Human-centered Sentiment Analysis11. An Interactive Tutoring System for Older Adults - Learning with New Apps12. Irony and Sarcasm Detection13. Concluding Remarks

• ISBN: 978-0-323-90535-0
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 350
• Fecha Publicación: 01/09/2022
• Nº Volúmenes: 1
• Idioma: Inglés