Analysing Public Sentiments Regarding COVID-19 Vaccines: A Sentiment Analysis Approach

Amjad, Arslan; Qaiser, Shahzad; Anwar, Aamir; Ijaz-ul-Haq,; Ali, Ramsha

doi:10.1109/isc253183.2021.9562904

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…RF combines the decision trees, whereas SVM draws a hyperplane for decision boundary. LSTM works very much like RNN at a very high level [ 28 , 41 ]. It consists of three parts which are known as gates of LSTM namely forget gate, input gate, and output gate.…”

Section: Methodsmentioning

confidence: 99%

“…It has been observed in the literature that most of the researchers have tested their emotion recognition methods on AMIGOS [ 1 , 3 , 7 , 8 , 11 , 12 , 21 , 38 , 39 , 40 ], DEAP [ 10 , 14 , 18 , 20 ], and DREAMER [ 7 , 8 , 10 ]. Furthermore, most commonly used physiological signals in the literature are EEG, ECG, and GSR [ 3 , 8 , 11 , 18 , 20 , 21 , 33 , 41 ]. These physiological signals have a complex and non-stationary nature.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals

Awan

Usman

Khalid

et al. 2022

Sensors

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Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection; therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The proposed system is trained and tested on the AMIGOS dataset with k-fold cross-validation. The proposed system obtained the highest accuracy of 94.5% and shows improved results of the proposed method compared with other state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals

Awan

Usman

Khalid

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

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“…The authors in [159] were among the first to recognise the issue and demonstrate that utilising realistic imbalanced datasets resulted in classifier construction that performed significantly better in reality. SMOTE was recently applied to over-sampled text representations built by a recursive neural tensor network to utilise an imbalanced dataset for emotion classification [160]. Class overlapping of different emotions makes it challenging to classify learner emotions accurately, and learners' sentences such as "Oh, I made a mistake" depict characteristics of "sad" and "angry" classes, which become difficult for the machine learning algorithm to classify.…”

Section: Emotion Classes Overlappingmentioning

confidence: 99%

Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning

Anwar,

Rehman,

Nasralla

et al. 2023

Education Sciences

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In recent years, the rapid growth of online learning has highlighted the need for effective methods to monitor and improve student experiences. Emotions play a crucial role in shaping students’ engagement, motivation, and satisfaction in online learning environments, particularly in complex STEM subjects. In this context, sentiment analysis has emerged as a promising tool to detect and classify emotions expressed in textual and visual forms. This study offers an extensive literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique on the role of sentiment analysis in student satisfaction and online learning in STEM subjects. The review analyses the applicability, challenges, and limitations of text- and facial-based sentiment analysis techniques in educational settings by reviewing 57 peer-reviewed research articles out of 236 articles, published between 2015 and 2023, initially identified through a comprehensive search strategy. Through an extensive search and scrutiny process, these articles were selected based on their relevance and contribution to the topic. The review’s findings indicate that sentiment analysis holds significant potential for improving student experiences, encouraging personalised learning, and promoting satisfaction in the online learning environment. Educators and administrators can gain valuable insights into students’ emotions and perceptions by employing computational techniques to analyse and interpret emotions expressed in text and facial expressions. However, the review also identifies several challenges and limitations associated with sentiment analysis in educational settings. These challenges include the need for accurate emotion detection and interpretation, addressing cultural and linguistic variations, ensuring data privacy and ethics, and a reliance on high-quality data sources. Despite these challenges, the review highlights the immense potential of sentiment analysis in transforming online learning experiences in STEM subjects and recommends further research and development in this area.

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“…From the publications (n = 47), eight studies compared machine learning models and chose the best-performing algorithm. In these comparisons, SVM [43,44,70] and LR [43,46,68] stood out with highly accurate performances. When comparing deep learning models and machine learning models, BERT showed an outstanding performance [51,55,64,69], whereas in comparison to deep learning models in [61], BiLSTM yielded the best performance.…”

mentioning

confidence: 92%

Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends

et al. 2022

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Pandemics and infectious diseases are overcome by vaccination, which serves as a preventative measure. Nevertheless, vaccines also raise public concerns; public apprehension and doubts challenge the acceptance of new vaccines. COVID-19 vaccines received a similarly hostile reaction from the public. In addition, misinformation from social media, contradictory comments from medical experts, and reports of worse reactions led to negative COVID-19 vaccine perceptions. Many researchers analyzed people’s varying sentiments regarding the COVID-19 vaccine using artificial intelligence (AI) approaches. This study is the first attempt to review the role of AI approaches in COVID-19 vaccination-related sentiment analysis. For this purpose, insights from publications are gathered that analyze the (a) approaches used to develop sentiment analysis tools, (b) major sources of data, (c) available data sources, and (d) the public perception of COVID-19 vaccine. Analysis suggests that public perception-related COVID-19 tweets are predominantly analyzed using TextBlob. Moreover, to a large extent, researchers have employed the Latent Dirichlet Allocation model for topic modeling of Twitter data. Another pertinent discovery made in our study is the variation in people’s sentiments regarding the COVID-19 vaccine across different regions. We anticipate that our systematic review will serve as an all-in-one source for the research community in determining the right technique and data source for their requirements. Our findings also provide insight into the research community to assist them in their future work in the current domain.

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Analysing Public Sentiments Regarding COVID-19 Vaccines: A Sentiment Analysis Approach

Cited by 7 publications

References 25 publications

An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals

An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals

Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning

Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends

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