The COVID-19 pandemic affected the whole world and changed every human life. Its devastation begins in China Wuhan and spreads worldwide, ruining human lives, health, and economies. The World Health Organization (WHO) [1] declared the COVID-19 pandemic on March 11, 2020, due to its destructive behavior. After discerning the rapid spread of COVID-19 [2], all nations and their governments adopted harsh preventive measures to manage this terrible epidemic and stop it from spreading. About 3.9 billion people have been affected and under lockdown or quarantined in their houses, and international travelers have been quarantined in quarantine centers reserved by different countries to contain the virus [3]. After the surge of COVID-19 cases, every field of life has faced troubles, and this disease disturbed their lives. Its aftershocks will be observed and felt for many years. The student community is one of the influential groups that has been disturbed pathetically and is missing from the vulnerable group’s list. This vulnerable group of international students has been facing social and economic obstacles, and no one has addressed their issues globally [4]. Globally lockdown policies were also affected, and universities shut down their campuses by following the government’s directives. Developed countries financially helped their citizens in this time of pandemic emergency. Unluckily, international students suffer social and economic costs and are ignored by the authorities [5,6]. International students from developing countries have already experienced difficulties with financial circumstances to continue their studies abroad [7]. The sudden shutdown of university campuses has created problems for students from developing countries. Most of the work found in the literature is survey-based, and the researchers performed statistical analysis on the collected data. A study revealed that 13% of students had faced late graduation concerns, and 4% of students lost their jobs due to the COVID-19 pandemic. Another survey [8] was conducted at “education.com” to evaluate “how COVID-19 affected your study abroad plans”, in which they estimated that 4.7% of students intend to cancel their study abroad plans. The number of students who returned to their homes due to the shutdown of university campuses and now want to return to their universities to resume their studies is 47.2%. Moreover, 68.2% of students are interested in studying abroad online. Before the COVID-19 situation, a global investment of $18.66 billion in 2019 and $350 was estimated to reach 2025 in educational technology. Over the decades, social media has become an active technological tool and a news and communication channel for the general public worldwide. It plays an influential role in the COVID-19 pandemic by dismantling information around the globe [9]. The public has used the social media platform more frequently during the COVID-19 pandemic to express their sentiments. The information about any issue and trends on social media spread as COVID-19 spread rapidly. We analyzed many trends that international students influence by using different hashtags like “#takeusback” to address their study regarding issues [4,10]. They want to return to their universities to continue their studies. To the best of our knowledge, there is no study yet to analyze and understand international students’ perceptions and problems during the COVID-19 pandemic using text mining. This paper has addressed and explored students’ sentiments and aspects regarding their financial distress, emotional distress, self-worth, and academic performance distress. The effect of COVID-19 on international students is heterogeneous. This study aims to answer the following research questions.

How does international students’ perception change due to COVID-19, and what problems do they face in this pandemic?

To answer the above research questions and uncover international students’ issues and views, NLP and Artificial Intelligence (AI) models are used widely to process textual data [11]. We proposed an ensemble deep learning framework (SABAC) for topic extraction, annotation, and classification. The purpose of SABAC is to annotate a large amount of unlabeled textual data automatically according to the situational aspects. In traditional text mining, most of the data is labeled and balanced, and labeling the unbalanced data according to the situational aspects is a laborious and time-consuming task. Therefore, we proposed three layers based SABAC framework. In the first layer of SABAC, we used an ensemble deep learning-based four different models to analyze international students’ tweets to explore and extract their situational emotions regarding their situation in the COVID-19 pandemic. We proposed and implemented an annotation algorithm in the second layer to annotate the extracted aspect terms as class labels. The third layer of SABAC used annotated tweets, which were annotated from the previous layer for the classification using deep learning algorithms in an ensemble learning manner to classify students’ textual data according to extracted class labels. In addition to the above-mentioned two research questions, we aim to answer the following research questions to examine issues of international students.

Does our proposed framework improve the aspect extraction process from textual feedback given by international students?

Analyzing the student’s tweets about the uncertainty in their future goals due to the COVID-19 pandemic is necessary to uncover hidden aspects in their feedback. Higher authorities can take essential measures regarding their study plans by analyzing international students’ views. This study will help understand the factors and issues regarding students’ situational information. Our main contributions to this paper are as follows:

We scrapped and collected Twitter dataset in which international students expressed their situational perceptions and opinion, and we named our dataset Student-COVID-19.

In the next section of this paper, we discussed related works. It is followed by a description of the research methodology, procedures, and data collection in Section 3. Results and discussion are presented in Section 4. Finally, the research conclusion is in Section 5.

Since the COVID-19 pandemic started, the interest of researchers has diverted to explore and analyze the effect of COVID-19 from many perspectives. China was the first country to force the regional lockdown strategy in Hubei province, especially Wuhan city, for 76 days [12]. Quarantine centers use IoT systems to monitor the suspected individuals and restrict them from interactions [13]. According to [14], the absence of these social connections leads to depression, anxiety, extreme mental stress, and many other aspects of life. In contrast to mental health, the authors [15] surveyed Liaoning Province, mainland China, to assess the influence of COVID-19 on people’s mental health. According to their findings, 52.1% of the 263 participants were terrified and worried due to the epidemic. However, 53.3% of those polled did not feel helpless due to the pandemic. Nowadays, deep learning is being used in various domains of life to improve performance, including face emotion recognition [16], comparative analysis of social communication apps [17], to evaluate the business insides [18], and many more. In this study, we reviewed previous work on text classification using ensemble deep learning techniques Table 1.

The authors [3,32] used machine learning and natural language processing techniques to analyze the 840000 tweets. Their sentiment results showed that the death and lockdown due to the COVID-19 are the aspects that mainly caused stress, anxiety, and Trauma among Indian citizens. COVID-19 increased depression, anxiety, and stress levels in individual life. Their results showed that the lockdown in the country increased depression in individuals. To understand the sentiment of an individual through social media posts, the authors of [33] used an adaptive model for automatic hate speech identification that is ensemble learning-based, enhancing cross-dataset generalization. Base learners in ensemble learning are reliant on the outcomes of base learners in the past. Furthermore, we reported previous ensemble studies and their implementation for text classification using machine learning, as shown in Table 2. As COVID-19 was declared a pandemic by WHO, people started sharing their opinion about mental stress and anxiety due to COVID-19 on Twitter [34]. Meanwhile, to analyze the students’ mental health and behavior toward COVID-19, the authors [35] surveyed 1182 students of different age groups. Their findings revealed that students used various social media platforms and other coping techniques to minimize worry and mental stress and obtained aid from their friends and family. Due to COVID-19, universities led to online classes, and students were asked to leave dorms; hence, students faced mental health issues. To examine the students’ mental health and evaluate their online academic performance, the study [36] conducted a general health Questionnaire-based survey among 1123 participants. The results of their experiments were considered significant at a p-value set of 0.05. 76.96% of the participants manifested psycho-pathological symptoms measured by this survey. To address and predict international students’ satisfaction, the researchers [37] gathered survey data from 425 students from different departments in Hong Kong. They performed different machine learning algorithms like regression and random forest; however, they achieved the highest accuracy using elastic net regression, and 65.2% explained variance. Their findings reported that it is critical to rule out the underlying reasons why students from different programs prefer face-to-face learning.

This section has discussed significant components of the manuscript, including dataset collection, pre-processing, feature extraction using several NLP techniques, and statistical analysis. The current study consists of two parts: In the first part, we performed aspect extraction using an ensemble learning approach to explore students’ situational perceptions or aspect terms from the Tweets. The proposed Emergency Situational Awareness (ESA) model is a fusion of different deep learning and machine learning models to improve Situational Understanding (SU), as shown in Fig. 1. We further divided our approach into three layers; in the first layer, we used different deep learning models as weak learners (weak classifiers) for Aspect Extraction (AE) from textual data using the ensemble learning approach. After extracting situational aspects from the dataset, we performed annotation of each tweet according to its extracted aspect term using aspect2class Algorithm 1 in the second layer of the proposed SABAC framework. The third layer includes training of deep learning weak classifiers using annotated Tweets according to aspect terms as class labels to perform classification. In this manner, our proposed model is different from other existing solutions, and the results of deep weak classifiers are passed to machine learning-based meta-classifiers as the proposed ensemble technique for the final prediction of class labels on the test dataset.

All experiments in this paper are conducted on Intel(R) Celeron(R) CPU N3150 @ 1.60 GHz. The operating system is Windows 64-bit, Python 3.6.6, TensorFlow deep Learning framework 1.8.0, and CUDA 10.1.

This study focuses on a novel Situational Aspect-Based Annotation and Classification (SABAC) framework to uncover international students’ situational aspects. As discussed in the methodology section, our proposed framework consists of three layers. The dataset used for this study was collected from Twitter using diffident hashtags represented by trends on Twitter from international students.

In the first layer, we used four deep learning models, such as LDA, BERT, HDP, and Zero-Shot Learning, as base learner (weak classifiers) models in the ensemble technique to mine accurate aspect terms from the Twitter dataset. The experimental results in extracted topics with their relevant terms and coherence score are shown in Tables 6–9.

Assessing the results shown in Tables 6–9, we can conclude that on the Student-COVID-19 dataset, the SABAC provides better results and coherent topics by selecting six topics and five relevant terms. Our topic modeling techniques’ performance according to each extracted topic is reported using a word cloud. In addition, the words that dominate the word cloud are most likely directly related to the topic term of the word cloud, as we can see in Fig. 5a reporting Topic-0, which talks about the “returning to school” of international students.

There are some dominant words in Fig. 5a that represent the topic of returning to school, such as “school” “open” “back” and “border” these words illustrate the issue of international students about their returning to school for physical classes.

(3)cv=∑i<j score(wi,wj)

Similarly, Figs. 5b, 5c, 5d and 5f represents Topic-1, Topic-2, Topic-3, Topic-4, and Topic-5, which show Financial Stress, Health, Online Education, Stress, and uncertainty, respectively. In contrast, the word cloud for each topic (Topic-1 to Topic5) contains dominant words representing each topic’s authenticity and relevant term score for each topic term. The topic extraction and relevant terms extraction results are shown below using Bert-Topic Library. For the topic extraction and modeling, we used different deep-learning approaches. The complete list of models and their parameters used in this study is shown in Table 4. Similarly, we used other deep learning models to evaluate the topic modeling results. The complete list of models and their architecture summary is shown in Table 5. The coherence score is for accessing the quality of learned topics through different models. We used a coherence score as it is essential to find the optimal number of topics because these topics will ultimately convert into labels for annotating the dataset. As shown in Eq. (3), for one topic, the words i, j is scored in ∑i<j score(wi,wj) have the highest probability of occurring for that topic. Furthermore, Tables 6–9 reported the extracted topics with relevant terms and coherence scores using Bert, LDA, HDP, and Zero-Shot Learning, respectively.

Moreover, the extracted topics have also demonstrated the situation of international students, such as mental stress or fear in the students whose degree is delayed due to lab work. Hence Table 8 also represents these terms as relevant terms, similar to Table 6, demonstrating the fear and being stuck. These terms also represent the international student’s situation as they are stuck in their rooms and home countries. Hence it represents fear of being late for graduation and unemployment. To better understand the extracted terms from the tweets, we also represent the co-occurrence of words within sentences, as shown in Fig. 7. We can conclude that our proposed SABAC model accurately extracts the situational aspects of international students to uncover their issues during the pandemic. The second layer of the proposed SABAC framework used Algorithm 1 to annotate a more significant number of unlabeled tweets into six mined aspect terms. Our proposed framework used the aspect2class algorithm and text similarity matrix to annotate unlabeled tweets into six mined aspect terms. In the third layer of the proposed framework, we used ensemble learning techniques to classify unlabeled Twitter tweets into six mined aspect terms. Using the ensemble technique, we used CNN, LSTM, Bi-LSTM, and RoBERTa models as base-learner classifiers in the third layer to classify our Twitter dataset into six mined aspect terms. The results of the base-learner classifier in the form of precision, recall, and f1-support are shown in Table 11. We used four different ensemble learning techniques in this classification layer of the proposed SABAC. These ensemble techniques include soft prediction, hard prediction, SABAC (soft prediction + VSF), and SABAC (Hard prediction + VSF). Moreover, we used logistic regression, support vector machine, naive Bayes, and random forest machine learning models as meta-classifiers in Layer-1 to evaluate the final prediction for aspect classification. The results of meta-classifiers’ using different ensemble techniques are shown in Table 10. We plotted the confusion matrix to depict our four meta-classifiers’ performances on the test dataset shown in Fig. 6.

Finally, to evaluate the performance of baseline models and the ensemble approach for each topic, we used different performance metrics such as precision, recall, and F1 score. The comparative values of performance metrics for each topic are shown in Table 11. We conducted several experiments on the previous benchmark datasets to validate our proposed model in the text classification task and compared the ensemble’s performance to the best individual baseline models. According to the Pareto principle, we divided each benchmark dataset into training and validation test sets with a ratio of 80% and 20%. The comparative result of an ensemble model on different benchmark datasets is shown in Table 12.

The proposed ensemble SABAC approach results on the previous benchmark datasets and our Students-COVID-19 dataset are demonstrated in Table 12, improving accuracy. Compared to the baseline models, the ensemble approaches improved the class prediction accuracy. Using the voting ensemble technique for final prediction, we achieved 82.45% accuracy on the SemEval-2018 dataset, 85.65% on the 20-newsgroup, 87.31% on the COVID19-Fake, and 91.31% on our collected Student-COVID dataset. Furthermore, using the stacking approach for final prediction, we achieved 83.21% accuracy on the SemEval-2018 dataset, 85.98% on the 20-newsgroup, 88.41% on the COVID19-Fake dataset, and 92.47 on our collected Student-COVID dataset. Likewise, we achieved the best results using the proposed SABAC approach in which we used ML models as meta-classifiers for final prediction, and we achieved 85.52% accuracy on the SemEval-2018 dataset, 86.76% accuracy on the 20-newsgroup, 88.98% accuracy on the COVID19-Fake dataset, and 93.21% on our collected Student-COVID-19 dataset. Moreover, Fig. 8 demonstrates the comparative results of used ensemble techniques on different datasets. The results reveal that the proposed SABAC framework is an improved approach that can perform robust topic extraction, annotation, and classification tasks. Finally, we compared the performance of our proposed approach with previous studies using ensemble techniques, as shown in Table 13.

In this study, we proposed an improved text mining approach named SABAC to uncover the situational topic terms from the international students’ tweets. Our findings show that international students face uncertainty, health, financial stress, job stress, online classes stress, and returning to school-related issues. To uncover these findings, an ensemble deep learning framework is proposed and implemented in this paper. Using our proposed framework, we have performed topic modeling to extract aspect terms and performed annotation on unlabeled tweets. After labeling the unlabeled dataset, we performed classification using deep learning models using ensemble stacking and boosting techniques. We achieved 89.94% accuracy on our proposed baseline soft prediction, and performance has been enhanced using Volatile Stopwords Filtering (VSF) method. We gained promising results using our proposed SABAC + VSF technique. We achieved 93.21% accuracy on our proposed model and compared our results with the latest reported work in the text mining domain. The study concluded that our proposed SABAC framework would help the authorities appropriately understand international students’ situational aspects and take the necessary steps for students in the COVID-19 pandemic. In future work, we will like to improve the accuracy of SABAC by handling high dimensional feature space on textual datasets transfer-learning techniques.