Chapter Contributed to a Springer Book on Deep Learning and Sentiment Analysis
Julian Risch and Ralf Krestel
Our chapter Toxic Comment Detection in Online Discussions has been accepted for publication in a book titled Deep learning based approaches for sentiment analysis. It will appear later this year in Springer's book series Algorithms for Intelligent Systems. A pre-print of our contribution can be found here. Stay tuned for the final publication of the book!
The editor Dr. Basant Agarwal describes 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.