PSent20: An Effective Political Sentiment Analysis with Deep Learning Using Real-Time Social Media Tweets

Garg, Apar; Kaliyar, Rohit Kumar

doi:10.1109/icraie51050.2020.9358379

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…The machine learning methods include Naïve Bayes [7], Logistic Regression [13], Decision Tree [25], K-nearest neighbor (KNN) [13], and AdaBoost [26]. The deep learning methods include Gated Recurrent Unit (GRU) [27], Long Short-Term Memory (LSTM) [27], Bidirectional Long Short-Term Memory (BiLSTM) [28], Convolutional Neural Network-LSTM (CNN-LSTM) [29], and Convolutional Neural Network-BiLSTM (CNN-BiLSTM) [30]. methods on all three datasets.…”

Section: Experimental Results and Analysismentioning

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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Section: Experimental Results and Analysismentioning

confidence: 99%

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

et al. 2022

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“…Decision Tree [28] 62.34 69 62 59 KNN [27] 60.39 66 60 57 XGBoost [25] 68.11 71 68 67 AdaBoost [29] 69.94 71 70 69 Ensemble (LR+SVM+RF) [25] 74.93 75 75 75 GRU [30] 78.96 78 78 78 LSTM [30] 79.10 79 79 79 BiLSTM [31] 78.53 78 78 78 CNN-LSTM [32] 77.53 77 77 77 CNN-BiLSTM [33] 77…”

Section: Methodsmentioning

confidence: 99%

Sentiment Analysis With Ensemble Hybrid Deep Learning Model

et al. 2022

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The rapid development of mobile technologies has made social media a vital platform for people to express their feelings and opinions. Understanding the public opinions can be beneficial for business and political entities in making strategic decisions. In light of this, sentiment analysis plays an important role to understand the polarity of the public opinions. This paper presents an ensemble hybrid deep learning model for sentiment analysis. The proposed ensemble model comprises three hybrid deep learning models which are the combination of Robustly optimized Bidirectional Encoder Representations from Transformers approach (RoBERTa), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU). In the hybrid deep learning model, RoBERTa is responsible for projecting the textual input sequence into a representative embedding space. Thereafter, the LSTM, BiLSTM and GRU capture the long-range dependencies in the embedding given the class. The predictions by the hybrid deep learning model are then amalgamated by averaging ensemble and majority voting, further improving the overall performance in sentiment analysis. In addition to that, the data augmentation with GloVe pre-trained word embedding has also been applied to alleviate the imbalanced dataset problems. The experimental results show that the proposed ensemble hybrid deep learning model outshines the state-of-the-art methods with the accuracy of 94.9%, 91.77%, and 89.81% on IMDb, Twitter US Airline Sentiment dataset and Sentiment140 dataset, respectively.

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“…Garg and Kaliyar (2020) [10] conducted a comparison of eight machine learning techniques for political sentiment analysis using social media tweets. The evaluated techniques encompassed Decision Tree classifiers (DT) employing entropy and Gini Index, Logistic Regression, Naive Bayes (NB), Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep CNN.…”

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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PSent20: An Effective Political Sentiment Analysis with Deep Learning Using Real-Time Social Media Tweets

Cited by 4 publications

References 7 publications

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

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

Sentiment Analysis With Ensemble Hybrid Deep Learning Model

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

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