UID:
edoccha_9961204027802883
Format:
1 online resource (xviii, 252 pages) :
,
illustrations.
ISBN:
0-323-90535-8
,
0-323-90637-0
,
9780323906371
,
0323906370
Series Statement:
Hybrid computational intelligence for pattern analysis and understanding
Content:
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.
Additional Edition:
Print version: ISBN 0323905358
Additional Edition:
ISBN 9780323905350
Additional Edition:
Print version: Computational intelligence applications for text and sentiment data analysis ISBN 9780323905350
Language:
English
Bookmarklink
