Deep Learning in Sentiment Analysis: A Survey of Recent Architectures

Journal article


Abdullah, T. and Ahmet, A. 2022. Deep Learning in Sentiment Analysis: A Survey of Recent Architectures. ACM Computing Surveys. 55 (8), pp. 1-37. https://doi.org/10.1145/3548772
AuthorsAbdullah, T. and Ahmet, A.
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.
This survey applies a task-oriented taxonomy to recent trends in architectures with a focus on the theory, design and implementation. To the best of our knowledge, this is the only survey to cover state-of-the-art Transformer-based language models and their performance on the most widely used benchmark datasets. This survey paper provides a discussion of the open challenges in NLP and sentiment analysis. The survey covers five years from 1st Jul 2017 to 1st Jul 2022.

Keywordshuman and device integration; sentiment analysis ; deep learning architecture
Year2022
JournalACM Computing Surveys
Journal citation55 (8), pp. 1-37
PublisherACM
ISSN0360-0300
1557-7341
Digital Object Identifier (DOI)https://doi.org/10.1145/3548772
Web address (URL)http://dx.doi.org/10.1145/3548772
Output statusPublished
Publication dates23 Dec 2022
Publication process dates
Deposited03 Feb 2023
Permalink -

https://repository.derby.ac.uk/item/9wq3y/deep-learning-in-sentiment-analysis-a-survey-of-recent-architectures

  • 76
    total views
  • 0
    total downloads
  • 9
    views this month
  • 0
    downloads this month

Export as

Related outputs

Virtual, augmented reality and learning analytics impact on learners, and educators: A systematic review
Sakr, A. and Abdullah, T. 2024. Virtual, augmented reality and learning analytics impact on learners, and educators: A systematic review. Education and Information Technologies. pp. 1-50. https://doi.org/10.1007/s10639-024-12602-5
Improving MOOCs experience using Learning Analytics and Intelligent Conversational Agent
Abdullah, T. and Sakr, A. 2021. Improving MOOCs experience using Learning Analytics and Intelligent Conversational Agent. in: Caballé, S., Demetriadis, S., Gómez-Sánchez, E., Papadopoulos, P. and Weinberger, A. (ed.) Intelligent Systems and Learning Data Analytics in Online Education Academic Press. pp. 47-70
Big data analytics in healthcare: A cloud based framework for generating insights
Anjum, Ashiq, Aizad, Sanna, Arshad, Bilal, Subhani, Moeez, Davies-Tagg, Dominic, Abdullah, Tariq and Antonopoulos, Nikolaos 2017. Big data analytics in healthcare: A cloud based framework for generating insights. in: Springer.
Traffic monitoring using video analytics in clouds
Abdullah, Tariq, Anjum, Ashiq, Tariq, M. Fahim, Baltaci, Yusuf and Antonopoulos, Nikolaos 2014. Traffic monitoring using video analytics in clouds. IEEE. https://doi.org/10.1109/UCC.2014.12
Video stream analysis in clouds: An object detection and classification framework for high performance video analytics
Anjum, Ashiq, Abdullah, Tariq, Tariq, M. Fahim, Baltaci, Yusuf and Antonopoulos, Nikolaos 2016. Video stream analysis in clouds: An object detection and classification framework for high performance video analytics. IEEE Transactions on Cloud Computing. https://doi.org/10.1109/TCC.2016.2517653