Sentiment and Emotion in Social Media COVID-19 Conversations: SAB-LSTM Approach

Kumar, D. Ashok; Chinnalagu, Anandan

doi:10.1109/smart50582.2020.9337098

Cited by 21 publications

(8 citation statements)

References 10 publications

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“…In the same way, Kumar et al [29] suggested a Sentiment Analysis Bidirectional-LSTM (SAB-LSTM). The model comprises 196 Bi-LSTM units, 128 Embedding layers, four thick layers, and a SoftMax activation function in the classification layer.…”

Section: Deep Learningmentioning

confidence: 99%

DeBERTa-GRU: Sentiment Analysis for Large Language Model

Assiri,

Gumaei,

Mehmood

et al. 2024

CMC

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Modern technological advancements have made social media an essential component of daily life. Social media allow individuals to share thoughts, emotions, and ideas. Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive, negative, neutral, or any other personal emotion to understand the sentiment context of the text. Sentiment analysis is essential in business and society because it impacts strategic decision-making. Sentiment analysis involves challenges due to lexical variation, an unlabeled dataset, and text distance correlations. The execution time increases due to the sequential processing of the sequence models. However, the calculation times for the Transformer models are reduced because of the parallel processing. This study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their limitations. In particular, the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers (BERT) attention (DeBERTa) and the Gated Recurrent Unit (GRU) for sentiment analysis. Using the Decoding-enhanced BERT technique, the words are mapped into a compact, semantic word embedding space, and the Gated Recurrent Unit model can capture the distance contextual semantics correctly. The proposed hybrid model achieves F1-scores of 97% on the Twitter Large Language Model (LLM) dataset, which is much higher than the performance of new techniques.

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Section: Deep Learningmentioning

confidence: 99%

DeBERTa-GRU: Sentiment Analysis for Large Language Model

Assiri,

Gumaei,

Mehmood

et al. 2024

CMC

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show abstract

“…Ni et al [23] construct a sentence sentiment analysis system based on the GloVe word vector model and recurrent neural networks, and proposed a neural network model combining LSTM and GRU in order to overcome the shortcomings of recurrent neural networks that cannot learn long-term information about text. SAB-LSTM [13] uses extension of BiLSTM with additional layers to process long text of social media posting, news articles. Some other methods such as GRU, another variant of LSTM to solve specific problems, SLCABG [38] uses CNN to extract the important features in the input matrix, and then uses BiGRU to consider the order information of the input text to extract text context features.…”

Section: Lstmmentioning

confidence: 99%

A Multi-Channel Text Sentiment Analysis Model Integrating Pre-training Mechanism

Liang

Jin

et al. 2023

ITC

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The number of tourist attractions reviews, travel notes and other texts has grown exponentially in the Internet age. Effectively mining users’ potential opinions and emotions on tourist attractions, and helping to provide users with better recommendation services, which is of great practical significance. This paper proposes a multi-channel neural network model called Pre-BiLSTM combined with a pre-training mechanism. The model uses a combination of coarse and fine- granularity strategies to extract the features of text information such as reviews and travel notes to improve the performance of text sentiment analysis. First, we construct three channels and use the improved BERT and skip-gram methods with negative sampling to vectorize the word-level and vocabulary-level text, respectively, so as to obtain more abundant textual information. Second, we use the pre-training mechanism of BERT to generate deep bidirectional language representation relationships. Third, the vectors of the three channels are input into the BiLSTM network in parallel to extract global and local features. Finally, the model fuses the text features of the three channels and classifies them using SoftMax classifier. Furthermore, numerical experiments are conducted to demonstrate that Pre-BiLSTM outperforms the baselines by 6.27%, 12.83% and 18.12% in average in terms of accuracy, precision and F1-score.

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“…Likewise, [16] proposed a Sentiment Analysis Bidirectional Long-Short Term Memory (SAB-LSTM). The model consists of 196 Bi-LSTM units, 128 Embedding layers, 4 dense layers and classification layer with SoftMax activation function.…”

Section: B Deep Learningmentioning

confidence: 99%

RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network

et al. 2022

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Due to the rapid development of technology, social media has become more and more common in human daily life. Social media is a platform for people to express their feelings, feedback, and opinions. To understand the sentiment context of the text, sentiment analysis plays the role to determine whether the sentiment of the text is positive, negative, neutral or any other personal feeling. Sentiment analysis is prominent from the perspective of business or politics where it highly impacts the strategic decision making. The challenges of sentiment analysis are attributable to the lexical diversity, imbalanced dataset and long-distance dependencies of the texts. In view of this, a data augmentation technique with GloVe word embedding is leveraged to synthesize more lexically diverse samples by similar word vector replacements. The data augmentation also focuses on the oversampling of the minority classes to mitigate the imbalanced dataset problems. Apart from that, the existing sentiment analysis mostly leverages sequence models to encode the long-distance dependencies. Nevertheless, the sequence models require a longer execution time as the processing is done sequentially. On the other hand, the Transformer models require less computation time with parallelized processing. To that end, this paper proposes a hybrid deep learning method that combines the strengths of sequence model and Transformer model while suppressing the limitations of sequence model. Specifically, the proposed model integrates Robustly optimized BERT approach and Long Short-Term Memory for sentiment analysis. The Robustly optimized BERT approach maps the words into a compact meaningful word embedding space while the Long Short-Term Memory model captures the longdistance contextual semantics effectively. The experimental results demonstrate that the proposed hybrid model outshines the state-of-the-art methods by achieving F1-scores of 93%, 91%, and 90% on IMDb dataset, Twitter US Airline Sentiment dataset, and Sentiment140 dataset, respectively.

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Sentiment and Emotion in Social Media COVID-19 Conversations: SAB-LSTM Approach

Cited by 21 publications

References 10 publications

DeBERTa-GRU: Sentiment Analysis for Large Language Model

DeBERTa-GRU: Sentiment Analysis for Large Language Model

A Multi-Channel Text Sentiment Analysis Model Integrating Pre-training Mechanism

RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network

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