Sentiment analysis using deep learning architectures: a review

By Ashima Yadav, Dinesh Kumar Vishwakarma. Artificial Intelligence Review, 2019

739 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b1e25e6dcf66029952b4f925c81848c04c97f2b6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

A review on sentiment analysis from social media platforms (2023, methodology): “Different from these surveys, Ashime et al. Ashima & Kumar (2020) in 2019 aimed to cover the significant and widespread approaches which are introduced in the field of sentiment analysis using deep learning – an extensive work including over 130 research papers, that provides a detailed survey of…”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “Soleymani et al. (2017) and Yadav and Vishwakarma (2020) on sentiment classification have been published.”
What Does it Mean for a Language Model to Preserve Privacy? (2022, background): “The following is a short list of some common language model tasks, which are the foundation of many of LM applications: part of speech (POS) tagging and parsing [25], optical character recognition (OCR) [65], automatic speech recognition [74], natural language generation [11], sentiment analysis [97”
Sentiment Analysis in the Era of Large Language Models: A Reality Check (2023, background): “Sentiment Analysis SA has received lots of attention since its early appearance (Turney, 2002; Yu and Hatzivassiloglou, 2003; Hu and Liu, 2004) and remained an active research area in the field of NLP nowadays (Liu, 2015; Poria et al., 2020; Yadav and Vishwakarma, 2020).”
Natural Language Processing Advancements By Deep Learning: A Survey (2020, background): “[146] provides a more recent review of the sentiment analysis methods relying on deep learning and gives an insightful discussion on the drawbacks as well as merits of deep learning methods for sentiment analysis.”