Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3083
Title: Deep Learning for Code-Mixed Text Mining in Social Media: A Brief Review
Authors: Panchendraraja, R
Saxena, A
Keywords: Code-mix
Text mining
Deep learning
Natural language processing
Social media
Issue Date: 19-Sep-2022
Publisher: Springer, Cham
Citation: Panchendrarajan, R., Saxena, A. (2022). Deep Learning for Code-Mixed Text Mining in Social Media: A Brief Review. In: Hong, TP., Serrano-Estrada, L., Saxena, A., Biswas, A. (eds) Deep Learning for Social Media Data Analytics. Studies in Big Data, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-10869-3_3
Series/Report no.: Deep Learning for Social Media Data Analytics;pp 45–63
Abstract: The advent of social media in day-to-day life has made communications between people more often and easier than ever before. Analyzing the content in social media has opened up a massive amount of research and commercial opportunities. However, the content in social media is noisy and multi-lingual, which postures computational challenges ahead. Especially, the non-native English speakers and writers tend to mix their native language with English while generating social media content. Thus it requires a comprehensive prepossessing of text, including the identification of language for many language processing applications. In the area of language processing, deep learning has shown to be very successful, and the latest research works have witnessed the adoption of deep learning solutions to cater to the challenges in analyzing code-mixed text. Here, we highlight a comprehensive study of deep learning techniques used for analyzing the code-mix text of social media to understand the state-of-the-art and existing research challenges. We will discuss several applications of code-mixed text analysis and future directions.
URI: https://rda.sliit.lk/handle/123456789/3083
ISBN: 978-3-031-10868-6
Appears in Collections:Department of Computer Systems Engineering
Research Papers - Dept of Computer Systems Engineering
Research Papers - SLIIT Staff Publications

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