Computational Intelligence Applications for Text and Sentiment Data Analysis (Hybrid Computational Intelligence for Pattern Analysis and Understanding) - Softcover

Softcover

ISBN 10: 0323905358 ISBN 13: 9780323905350

Publisher: Academic Press, 2023

Synopsis

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

About this title

About the Authors

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.

From the Back Cover

Sentiment Analysis (SA) has emerged as one of the fastest growing research trends in the last few years as exponential numbers of global internet users are expressing their opinions through various social media platforms across a wide range of issues. Emotion and polarity prediction, from customer feedback through various social media such as Facebook, Twitter, etc., is an important emerging subfield of predictive modelling. Most recently, many big companies have been using various computational intelligence algorithms to understand customers’ attitudes towards their products and in order to successfully run their businesses. In this way, Sentiment Analysis has emerged as a critical tool in decision making because social media platforms are used as the most preferred medium to record such issues.

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 multi-faceted data. It 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, 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.

Edition details

• Publisher: Academic Press
• Publication date: 2023
• Language: English
• ISBN 10: 0323905358
• ISBN 13: 9780323905350
• Binding: Paperback
• Edition number: 1
• Number of pages: 270
• Editor: Das Dipankar, Kolya Anup Kumar, Basu Abhishek, Sarkar Soham