A RNN Based Parallel Deep Learning Framework for Detecting Sentiment Polarity from Twitter Derived Textual Data

Chowdhury, Sakib; Rahman, Md. Latifur; Ali, Shams Nafisa; Alam, Md. Jahin

doi:10.1109/icece51571.2020.9393137

Cited by 5 publications

(1 citation statement)

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“…In a study by Chowdhury et al (2020) [12], the authors introduced a Bi-LSTM model for sentiment analysis on a dataset of 336 Twitter posts. Training and evaluation were conducted using the publicly available Kaggle dataset called Twitter US Airline Sentiment.…”

Section: B Deep Learningmentioning

confidence: 99%

MPNet-GRUs: Sentiment Analysis With Masked and Permuted Pre-Training for Language Understanding and Gated Recurrent Units

Kai Ning Loh,

Poo Lee,

Song Ong

et al. 2024

IEEE Access

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Sentiment analysis, a pivotal task in natural language processing, aims to discern opinions and emotions expressed in text. However, existing methods for sentiment analysis face various challenges such as data scarcity, complex language patterns, and long-range dependencies. In this paper, we propose MPNet-GRUs, a hybrid deep learning model that integrates three key components: MPNet, BiGRU, and GRU. MPNet, a transformer-based pre-trained language model, enhances language understanding through masked and permuted pre-training. BiGRU and GRU, recurrent neural networks, capture long-term dependencies bidirectionally and unidirectionally. By combining the strengths of these models, MPNet-GRUs aims to provide a more effective and efficient solution for sentiment analysis. Evaluation on three benchmark datasets reveals the superior performance of MPNet-GRUs: 94.71% for IMDb, 86.27% for Twitter US Airline Sentiment, and 88.17% for Sentiment140, demonstrating its potential to advance sentiment analysis.

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Section: B Deep Learningmentioning

confidence: 99%