Hitesh Nankani, Hritwik Dutta, Harsh Shrivastava, Rama Krishna P V N S, Debanjan Mahata, and Rajiv Ratn Shah, Springer (2020).


Sentiment analysis has empowered researchers and analysts to extract opinions of people regarding various products, services, events and other entities. This has been made possible due to an astronomical rise in the amount of text data being made available on the Internet, not only in English but also in many regional languages around the world as well, along with the recent advancements in the field of machine learning and deep learning. It has been observed that deep learning models produce the state-of-the-art prediction results without the need for domain expertise or handcrafted feature engineering, unlike traditional machine learning-based algorithms. In this chapter, we wish to focus on sentiment analysis of various low resource languages having limited sentiment analysis resources such as annotated datasets, word embeddings and sentiment lexicons, along with English. Techniques to refine word embeddings for sentiment analysis and improve word embedding coverage in low resource languages are also covered. Finally, we discuss the major challenges involved in multilingual sentiment analysis and explain novel deep learning-based solutions to overcome them.