Deep Learning in Sentiment Analysis: A Survey of Recent Architectures
Journal article
Authors | Abdullah, T. and Ahmet, A. |
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Abstract | Humans are increasingly integrated with devices that enable the collection of vast unstructured opinionated data. Accurately analysing subjective information from this data is the task of sentiment analysis (an actively researched area in NLP). Deep learning provides a diverse selection of architectures to model sentiment analysis tasks and has surpassed other machine learning methods as the foremast approach for performing sentiment analysis tasks. Recent developments in deep learning architectures represent a shift away from Recurrent and Convolutional neural networks and the increasing adoption of Transformer language models. Utilising pre-trained Transformer language models to transfer knowledge to downstream tasks has been a breakthrough in NLP. |
Keywords | human and device integration; sentiment analysis ; deep learning architecture |
Year | 2022 |
Journal | ACM Computing Surveys |
Journal citation | 55 (8), pp. 1-37 |
Publisher | ACM |
ISSN | 0360-0300 |
1557-7341 | |
Digital Object Identifier (DOI) | https://doi.org/10.1145/3548772 |
Web address (URL) | http://dx.doi.org/10.1145/3548772 |
Output status | Published |
Publication dates | 23 Dec 2022 |
Publication process dates | |
Deposited | 03 Feb 2023 |
https://repository.derby.ac.uk/item/9wq3y/deep-learning-in-sentiment-analysis-a-survey-of-recent-architectures
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