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Multilingual Sentiment Analysis

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Deep Learning-Based Approaches for Sentiment Analysis

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

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.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Languages_used_on_the_Internet.

  2. 2.

    https://translate.google.com.

  3. 3.

    http://colah.github.io/posts/2015-08-Understanding-LSTMs/.

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Authors and Affiliations

  1. MIDAS Lab, IIIT Delhi, New Delhi, India

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

  2. NSIT, New Delhi, India

    Hitesh Nankani & Hritwik Dutta

  3. Rustamji Institute of Technology, Gwalior, India

    Harsh Shrivastava

  4. IIIT, Bhubaneswar, India

    P. V. N. S. Rama Krishna

  5. Bloomberg, New York, USA

    Debanjan Mahata

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  1. Hitesh Nankani
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  2. Hritwik Dutta
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  4. P. V. N. S. Rama Krishna
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Correspondence to Rajiv Ratn Shah .

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Editors and Affiliations

  1. Indian Institute of Information Technology Kota (IIIT-Kota), Jaipur, Rajasthan, India

    Basant Agarwal

  2. Faculty of Science and Engineering, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD, Australia

    Richi Nayak

  3. Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Namita Mittal

  4. Department of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

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Nankani, H., Dutta, H., Shrivastava, H., Rama Krishna, P.V.N.S., Mahata, D., Shah, R.R. (2020). Multilingual Sentiment Analysis. In: Agarwal, B., Nayak, R., Mittal, N., Patnaik, S. (eds) Deep Learning-Based Approaches for Sentiment Analysis. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1216-2_8

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