Computational Intelligence Applications for Text and Sentiment Data Analysis -

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Résumé :
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....

Sommaire:
1. Introduction to Text and Sentiment Data Analysis
2. Natural Language Processing and Sentiment Analysis: Perspectives from Computational Intelligence
3. Applications and Challenges of Sentiment Analysis in Real Life Scenarios
4. Emotions Recognition of Students from Online and Offline Texts
5. Online Social Network Sensing Models
6. Identifying Sentiments of Hate Speech using Deep Learning
7. An Annotation System to Summarize Medical Corpus using Sentiment based Models
8. Deep learning-based Dataset Recommendation System by employing Emotions
9. Hybrid Deep Learning Architecture Performance on Large English Sentiment Text Data: Merits and Challenges
10. Human-centered Sentiment Analysis
11. An Interactive Tutoring System for Older Adults - Learning with New Apps
12. Irony and Sarcasm Detection
13. Concluding Remarks...