*Call for Book Chapter- Springer*

*Book Title*: Deep learning based approaches for sentiment analysis

*The scope of the book:*

With the exponential growth in the use of social media networks such as Twitter, Facebook, and many others, an astronomical amount of big data has been generated. A substantial amount of this user-generated data is in form of text such as reviews, tweets, and blogs that provide numerous challenges as well as opportunities to NLP (Natural Language Processing) researchers for discovering meaningful information used in various applications. Sentiment analysis is the study that analyses people's opinion and sentiment towards entities such as products, services, person, organisations etc. present in the text. Sentiment analysis and opinion mining is the most popular and interesting research problem.

In recent years, Deep Learning approaches have emerged as powerful computational models and have shown significant success to deal with a massive amount of data in unsupervised settings. Deep learning is revolutionizing because it offers an effective way of learning representation and allows the system to learn features automatically from data without the need of explicitly designing them. Deep learning algorithms such as deep autoencoders, convolutional and recurrent neural networks (CNN) (RNN), Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GAN) have reported providing significantly improved results in various natural language processing tasks including sentiment analysis.

Topics of Interest:

This book solicits contributions from the field of sentiment analysis using deep learning. Each book chapter should cover the solutions with the state-of-the-art and novel approaches for the sentiment analysis problems and challenges.

Topics to be discussed in this edited book include but not are limited to:

1. Introduction to the Deep learning algorithms and sentiment analysis 2. Recent trends and advances in deep learning based sentiment analysis 3. Deep Neural Networks for Sentiment Analysis of Short Texts 4. Learning word representations for sentiment analysis 5. Learning sentiment-specific word embeddings for sentiment analysis 6. Improving the performance of the sentiment analysis model using learning representation of out-of-vocabulary words 7. Deep neural network based sentiment analysis model for sarcastic tweets 8. A deep neural network for Cross-domain sentiment analysis 9. Financial Sentiment analysis using deep learning algorithms 10. Sentiment lexicon and knowledge-base construction for sentiment analysis 11. Aspect level sentiment analysis using deep neural network 12. Deep learning based Multi-Domain Sentiment Analysis 13. Fusion of linguistic and deep learning approaches for improved sentiment analysis model 14. Enhancing deep learning sentiment analysis with ensemble techniques 15. Transfer learning based sentiment analysis model for a specific domain 16. Spam Review analysis 17. Detecting hate speech in Twitter 18. Future Directions to research for Sentiment Analysis 19. Any other relevant deep learning based novel application

Important Dates:

Abstract Submission (abstract with TOC): December 31, 2018

Preliminary acceptance/rejection notification: January 15, 2019

Full chapter Submission: March 31, 2019

First review notification: May 15, 2019

Revised paper submission: June 15, 2019

Acceptance/Rejection notification: June 30, 2019

Camera Ready submission: July 15, 2019

*Editors*

1. Dr. Basant Agarwal, Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology Mangt. & Gramothan, Jaipur, India Email: basant@skit.ac.in

2. Dr. Richi Nayak, School of Electrical Engineering and Computer Science, Faculty of Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia. Email: r.nayak@qut.edu.au

3. Dr. Namita Mittal, Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur India. Email: nmittal.cse@mnit.ac.in

4. Dr. Srikanta Patnaik, Professor, Department of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India. Email: patnaik_srikanta@yahoo.co.in

*Submission Guidelines:*

Please send your one-page write up (with abstract of 300- 500 words and 6 keywords) of your Chapter along with tentative table of contents (TOC) through EasyChair portal at this link { https://easychair.org/conferences/?conf=dlsentianalysis2019%7D. Upon acceptance of the proposal, further instructions for submission guidelines according to the Springer will be communicated. Please feel free to contact any editor if you have any query.

“There are NO processing/publication charges for this Springer book”

~Best Regards

Basant