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.
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. 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
Dr. Dipankar Das is an Assistant Professor in the Computer Science
and Engineering Department, Jadavpur University, and Visveswaraya
Young Faculty, Ministry of Electronics and Information Technology
(MeitY), Government of India. Before that, he served as an
Assistant Professor in the Computer Science and Engineering
Department, National Institute of Technology (NIT), Meghalaya, and
Government of India from 2012 to 2014. His research interests are
in the area of Natural Language and Speech Processing, especially
in Emotion and Sentiment Analysis, Machine Translation, Social
Texts Analytics, Information Extraction, and Machine Learning, Data
Science, HCI, etc. He is leading several research projects such as
“Sevak – An Intelligent Indian Language Chatbot under IMPRINT II
research scheme, funded by Science & Engineering Research Board
(SERB), DST, Government of India, “Claim Detection and Verification
using Deep NLP: an Indian Perspective funded by Defence Research
and Development Organisation (DRDO), “Detect Behavioral
Maladjustments of Students through Sentiment Analysis from Social
Media, funded by UGC RUSA 2.0 and “Preparing annotated corpus of
three lesser known languages of West Bengal, funded by UGC RUSA
2.0, respectively.
Dr. Das has had more than 150 publications in top journals,
conferences and workshops and has served as an editor and author of
several books and book chapters. Dr. Anup Kumar Kolya, received his
Ph.D. (Engg) from Jadavpur University, India in 2015. He is
currently an Assistant Professor at the RCC Institute of
Information Technology, Kolkata, India. He served as post graduate
Program coordinator in the computer science and engineering
department from 4/4/2018 to 5/1/2020. Prior to this, he was a
senior research engineer at Jadavpur University, from 2/1/2009 to
1/5/2014. Before that, he served as a lecturer of computer science
and engineering department of Mallabhum Institute of Technology
Bishnupur, India from 2005-2006.
He is co-editor of one book. Moreover, has more than 40 research
publications in international journals, conference proceedings and
edited volumes to his credit. He has been a program committee
member, organizing committee member, publication chair, and
reviewer of various international journals, book chapters, and
conferences. His research interests include natural language
processing, social media data analysis, Internet of things and text
image data analysis. Dr. Abhishek Basu received his PhD (Engg) from
Jadavpur University, India in 2015. He is currently an Assistant
Professor and Faculty in Charge (Academics) at RCC Institute of
Information Technology, Kolkata, India. He served as Undergraduate
Program Coordinator and Head of the Department in the Electronics
and Communication Engineering Department (30/03/16 - 01/01/17 and
01/01/17 - 31/12/18 respectively). Prior to this, he was a lecturer
of Electronics and Communication Engineering at Guru Nanak
Institute of Technology, Kolkata, India from 2008-2009. Before
that, he served as a lecturer of Electronics and Communication
Engineering at Mallabhum Institute of Technology Bishnupur, India
from 2005-2007. He is co-author of a book and co-editor of two
books. Moreover, has more than 50 research publications in
international journals, conference proceedings and edited volumes
to his credit. He has also filed three Indian patents to date.
His research interests include digital image processing, visual
information hiding, IP protection technique, FPGA based system
design, low power VLSI Design and embedded system design. Dr. Basu
is a lifetime member of Indian Association for Productivity,
Quality & Reliability, India and Institute of Engineers (India). He
is also member of the IEEE and the International Association of
Engineers. Dr. Soham Sarkar received his PhD in Electronics and
Telecommunication Engineering in 2016 from Jadavpur University,
Kolkata. Dr. Soham Sarkar is currently serving as an Assistant
Professor in Department of Electronics and Communication
Engineering, RCC Institute of Information Technology, Kolkata,
India. He has more than 12 years of teaching experience in
Academics. His research interests include Evolutionary Algorithms,
Soft Computing Techniques, Pattern Recognition, and Digital Image
Processing. Dr. Sarkar has published his works in leading
international journals and conference proceedings. Dr. Sarkar has
400+ Google Scholar citations to date. He has been associated with
the program committees and organizing committees of several
renowned regular international conferences including ISSIP,
FUZZ-IEEE, SEMCCO, ICAPR, ICRCICN etc. He is also co-editor of one
book.
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