Deep Learning-Based Approaches for Sentiment Analysis

1. Front Matter

   Pages i-xii

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2. Application of Deep Learning Approaches for Sentiment Analysis

   • Ajeet Ram Pathak, Basant Agarwal, Manjusha Pandey, Siddharth Rautaray

   Pages 1-31

3. Recent Trends and Advances in Deep Learning-Based Sentiment Analysis

   • Ahmed Ahmet, Tariq Abdullah

   Pages 33-56

4. Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews

   • Danilo Dessí, Mauro Dragoni, Gianni Fenu, Mirko Marras, Diego Reforgiato Recupero

   Pages 57-83

5. Toxic Comment Detection in Online Discussions

   • Julian Risch, Ralf Krestel

   Pages 85-109

6. Aspect-Based Sentiment Analysis of Financial Headlines and Microblogs

   • Hitkul, Simra Shahid, Shivangi Singhal, Debanjan Mahata, Ponnurangam Kumaraguru, Rajiv Ratn Shah

   Pages 111-137

7. Deep Learning-Based Frameworks for Aspect-Based Sentiment Analysis

   • Ashish Kumar, Aditi Sharan

   Pages 139-158

8. Transfer Learning for Detecting Hateful Sentiments in Code Switched Language

   • Kshitij Rajput, Raghav Kapoor, Puneet Mathur, Hitkul, Ponnurangam Kumaraguru, Rajiv Ratn Shah

   Pages 159-192

9. Multilingual Sentiment Analysis

   • Hitesh Nankani, Hritwik Dutta, Harsh Shrivastava, P. V. N. S. Rama Krishna, Debanjan Mahata, Rajiv Ratn Shah

   Pages 193-236

10. Sarcasm Detection Using Deep Learning-Based Techniques

    • Niladri Chatterjee, Tanya Aggarwal, Rishabh Maheshwari

    Pages 237-258

11. Deep Learning Approaches for Speech Emotion Recognition

    • Anjali Bhavan, Mohit Sharma, Mehak Piplani, Pankaj Chauhan, Hitkul, Rajiv Ratn Shah

    Pages 259-289

12. Bidirectional Long Short-Term Memory-Based Spatio-Temporal in Community Question Answering

    • Nivid Limbasiya, Prateek Agrawal

    Pages 291-310

13. Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language

    • Savaş Yildirim

    Pages 311-319

This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.

 

• Sentiment Analysis
• Opinion Mining
• Deep Learning
• Text Mining
• Convolutional Neural Network
• Recurrent Neural Network
• Long Short-term Memory Networks
• Sentiment Mining
• Sentiment Classification

• Indian Institute of Information Technology Kota (IIIT-Kota), Jaipur, India

  Basant Agarwal

• Faculty of Science and Engineering, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia

  Richi Nayak

• Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India

  Namita Mittal

• Department of Computer Science and Engineering, SOA University, Bhubaneswar, India

  Srikanta Patnaik

Dr. Basant Agarwal is an Assistant Professor at the Indian Institute of Information Technology Kota (IIIT-Kota), India. He holds a Ph.D. from MNIT Jaipur, and worked as a Postdoc Research Fellow at the Norwegian University of Science and Technology (NTNU), Norway, under the prestigious ERCIM (European Research Consortium for Informatics and Mathematics) fellowship in 2016. He has also worked as a Research Scientist at Temasek Laboratories, National University of Singapore (NUS), Singapore. 

Dr. Richi Nayak holds an M.E. degree from the Indian Institute of Technology, Roorkee, India, and received her Ph.D. in Computer Science from the Queensland University of Technology (QUT), Brisbane, Australia, in 2001. She is currently an Associate Professor of Computer Science at QUT, where she is also Head of Data Science. She has been successful in attaining over $4 million in external research funding in the area of text mining over the past ten years. She is a consultant to anumber of government agencies in the area of data, text, and social media analytics projects. She is member of the steering committee of Australasian Data Mining in Australia (AusDM). She is the founder and leader of the Applied Data Mining Research Group at QUT. She has received a number of awards and nominations for teaching, research, and other activities. 

Dr. Namita Mittal is an Associate Professor at the Department of Computer Science and Engineering, MNIT Jaipur, India. She is a recipient of the Career Award for Young Teachers (CAYT) by AICTE. She has published numerous research papers in respected international conferences and journals, and has also authored a book on the topic of sentiment analysis in the Springer book series “Socio-Affective Computing”. She is an SMIEEE, and a member of ACM, CCICI, and SCRS. She has been involved in various FDPs/conferences/workshops, like the Ph.D. Colloquium FIRE 2017, and International Workshop on Text Analytics and Retrieval(WI 2018) in conjunction with Web Intelligence (WI), USA, to name a few.  

Dr. Srikanta Patnaik is a Professor at the Department of Computer Science and Engineering, Faculty of Engineering and Technology, SOA University, Bhubaneswar, India. He received his Ph.D. in Computational Intelligence from Jadavpur University, India, in 1999. Dr. Patnaik was the Principal Investigator of the AICTE-sponsored TAPTEC project “Building Cognition for Intelligent Robot” and the UGC-sponsored Major Research Project “Machine Learning and Perception using Cognition Methods”. He is the Editor-in-Chief of the International Journal of Information and Communication Technology and the International Journal of Computational Vision and Robotics. Dr. Patnaik is also the Editor of the Journal of Information and Communication Convergence Engineering, published by the Korean Institute of Information and Communication Engineering. He is also the Editor-in-Chief of Springer book series “Modeling andOptimization in Science and Technology”. 

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