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.
1. Computational Intelligence Applications for Text and Sentiment Data Analysis - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. List of contributors - p. Page vi 8. About the editors - p. Page vi 9. Preface - p. Page ix 10. List of Illustrations - p. Page xii 11. List of Tables - p. Page xiii 12. Chapter 1 : Sentiment analysis and computational intelligence - p. Page xiii 13. Chapter 2 : Natural language processing and sentiment analysis: perspectives from computational intelligence - p. Page 13 14. Chapter 3 : Applications and challenges of SA in real-life scenarios - p. Page 44 15. Chapter 4 : Emotions of students from online and offline texts - p. Page 73 16. Chapter 5 : Online social-network sensing models - p. Page 109 17. Chapter 6 : Learning sentiment analysis with word embeddings - p. Page 137 18. Chapter 7 : An annotation system of a medical corpus using sentiment-based models for summarization applications - p. Page 159 19. Chapter 8 : A comparative study of a novel approach with baseline attributes leading to sentiment analysis of Covid-19 tweets - p. Page 177 20. Chapter 9 : Sentiment-aware design of human–computer interactions: How research in human–computer interaction and sentiment analysis can lead to more user-centered systems? - p. Page 205 21. Chapter 10 : Towards the analyzing of social-media data to assess the impact of long lockdowns on human psychology due to the Covid-19 pandemic - p. Page 222 22. Chapter 11 : Conclusion - p. Page 237 23. Index - p. Page 242
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.
1. Computational Intelligence Applications for Text and Sentiment Data Analysis - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. List of contributors - p. Page vi 8. About the editors - p. Page vi 9. Preface - p. Page ix 10. List of Illustrations - p. Page xii 11. List of Tables - p. Page xiii 12. Chapter 1 : Sentiment analysis and computational intelligence - p. Page xiii 13. Chapter 2 : Natural language processing and sentiment analysis: perspectives from computational intelligence - p. Page 13 14. Chapter 3 : Applications and challenges of SA in real-life scenarios - p. Page 44 15. Chapter 4 : Emotions of students from online and offline texts - p. Page 73 16. Chapter 5 : Online social-network sensing models - p. Page 109 17. Chapter 6 : Learning sentiment analysis with word embeddings - p. Page 137 18. Chapter 7 : An annotation system of a medical corpus using sentiment-based models for summarization applications - p. Page 159 19. Chapter 8 : A comparative study of a novel approach with baseline attributes leading to sentiment analysis of Covid-19 tweets - p. Page 177 20. Chapter 9 : Sentiment-aware design of human–computer interactions: How research in human–computer interaction and sentiment analysis can lead to more user-centered systems? - p. Page 205 21. Chapter 10 : Towards the analyzing of social-media data to assess the impact of long lockdowns on human psychology due to the Covid-19 pandemic - p. Page 222 22. Chapter 11 : Conclusion - p. Page 237 23. Index - p. Page 242
