In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and ...processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.
Agriculture plays an important role in a country’s economy. The sector is challenged by many stresses, which led to huge loss in plant productivity worldwide. The ever-increasing population, rapid ...urbanization with shrinking agricultural lands, dramatic change in climatic conditions, and extensive use of agrochemicals in agricultural practices that caused environmental disturbances confront mankind of escalating problems of food security and sustainability in agriculture. Escalating environmental problems and global hunger have led to the development and adoption of genetic engineering and other conventional plant breeding approaches in developing stress-tolerant varieties of crops. However, these approaches have drawn flaws in their adoption as the process of generating tolerant varieties takes months to years in bringing the technology from the lab to the field. Under such scenario, sustainable and climate-smart agricultural practices that avail bacterial usage open the avenues in fulfilling the incessant demand for food for the global population. Ensuring stability on economic fronts, bacteria minimizes plant salt uptake by trapping ions in their exopolysaccharide matrix besides checking the expression of Na
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and high-affinity potassium transporters. Herein we describe information on salinity stress and its effect on plant health as well as strategies adopted by plant growth-promoting rhizobacteria (PGPR) in helping plants to overcome salinity stress and in mitigating loss in overall plant productivity. It is believed that acquisition of advanced knowledge of plant-beneficial PGPR will help in devising strategies for sustainable, environment-friendly, and climate-smart agricultural technologies for adoption in agriculture to overcome the constrained environmental conditions.
Online social networks (OSNs) are trendy and rapid information propagation medium on the web where millions of new connections either positive such as acquaintance, or negative such as animosity, are ...being established every day around the world. The negative links (or sometimes referred to as harmful connections) are mostly established by fake profiles as they are being created by minds with ill aims. Detecting negative (or suspicious) links within online users can better aid in the mitigation of fake profiles from OSNs.
A modified clustering coefficient formula, named as MutualClusteringCoefficient represented byMcc, is introduced to quantitatively measure the connectivity between the mutual friends of two connected users in a group. In this paper, a classification system based on mutual clustering coefficient and profile information of users has been presented to detect the suspicious links within the user communities. Profile information helps us to find the similarity between users. Different similarity measures have been employed to calculate the profile similarity between a connected user pair. Experimental results demonstrate that four basic and easily available features such as workw,educatione,home_townhtandcurrent_city(cc) along with MCC play a vital role in designing a successful classification system for the detection of suspicious links.
Social media platforms have been widely used as a communication tool where most of the population expresses their feelings and shares life experiences. Along with general information about the ...public, these platforms hold an ample amount of content related to depressed users and thus can generate sensitive social signals indicating if a person is suffering from some serious issues, such as self-harm, suicidal thoughts, or intention for an unlawful act. Early depression detection using advanced natural language processing (NLP), deep machine learning, and transfer learning techniques can assist in designing an efficient system to detect major depressive systems at an early stage. The current depression detection models are not enough to capture sensitive social signals indicating the true mood, personality, and behavior of an individual. Thus, making the current systems unsatisfactory. To address this life-threatening human-health problem, we propose an efficient artificial intelligence (AI) and deep learning (DL)-based model for identifying depressed individuals on social media platforms. The model employs hybrid feature-based behavioral-biometric signals captured using Word2Vec, term frequency-inverse document frequency (TF-IDF) models to learn a convolutional neural network (CNN) and long-short term memory (LSTM) models. The data are captured from multiple sources using advanced crawling strategies to have data variety in the corpus. Thus, making the proposed system effective across platforms. The Dataset produced by this study is the first of its kind with a variety of depressive signals from online social network (OSN) platforms including Facebook, Twitter, and YouTube. The experiments have shown that both DL models LSTM and CNN, and the hybrid (CNN + LSTM) models achieved promising results on all individual as well as combined datasets. Out of 24 experiments for Word2Vec LSTM and Word2Vec (CNN + LSTM) models, we achieved the accuracy of 99.02% and 99.01%, respectively, and recorded as best results outperforming all the existing approaches on performance measures such as recall, precision, accuracy, and <inline-formula> <tex-math notation="LaTeX">{F}1 </tex-math></inline-formula>-score. The Word2Vec-based features have been proved optimal features for detecting depressions symptoms on Facebook corpus (FC) and YouTube corpus (YC) by achieving an accuracy of 95.02% (with CNN) and 98.15% (with CNN + LSTM), respectively.
Social media platforms serve as communication tools where users freely share information regardless of its accuracy. Propaganda on these platforms refers to the dissemination of biased or deceptive ...information aimed at influencing public opinion, encompassing various forms such as political campaigns, fake news, and conspiracy theories. This study introduces a Hybrid Feature Engineering Approach for Propaganda Identification (HAPI), designed to detect propaganda in text-based content like news articles and social media posts. HAPI combines conventional feature engineering methods with machine learning techniques to achieve high accuracy in propaganda detection. This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). Hybrid feature engineering entails the amalgamation of various features, including Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BoW), Sentimental features, and tweet length, among others. Multiple Machine Learning classifiers undergo training and evaluation utilizing the proposed methodology, leveraging a selection of 40 pertinent features identified through the hybrid feature selection technique. All the selected algorithms including Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR) achieved promising results. The SVM-based HaPi (SVM-HaPi) exhibits superior performance among traditional algorithms, achieving precision, recall, F-Measure, and overall accuracy of 0.69, 0.69, 0.69, and 69.2%, respectively. Furthermore, the proposed approach is compared to well-known existing approaches where it overperformed most of the studies on several evaluation metrics. This research contributes to the development of a comprehensive system tailored for propaganda identification in textual content. Nonetheless, the purview of propaganda detection transcends textual data alone. Deep learning algorithms like Artificial Neural Networks (ANN) offer the capability to manage multimodal data, incorporating text, images, audio, and video, thereby considering not only the content itself but also its presentation and contextual nuances during dissemination.
Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify ...defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority over-sampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of F1-measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (AUC) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models’ F1-measure by 42.60%, 32.00% and 25.10% and AUC by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance.
People share their views and daily life experiences on social networks and form a network structure. The information shared on social networks can be unreliable, and detecting such kinds of ...information may reduce mass panic. Propaganda is a kind of biased or unreliable information that can mislead or intend to promote a political cause. The disseminators involved in spreading such information create a sophisticated network structure. Detecting such communities can lead to a safe and reliable network for the users. In this paper, a Boundary-based Community Detection Approach (BCDA) has been proposed to identify the core nodes in a propagandistic community that detects propagandistic communities from social networks with the help of interior and boundary nodes. The approach consists of two phases, one is to detect the community, and the other is to detect the core member. The approach mines nodes from the boundary as well as from the interior of the community structure. The leader Ranker algorithm is used for mining candidate nodes within the boundary, and the Constraint coefficient is used for mining nodes within the boundary. A novel dataset is generated from Twitter. About six propagandistic communities are detected. The core members of the propagandistic community are a combination of a few nodes. The experiments are conducted on a newly collected Twitter dataset consisting of 16 attributes. From the experimental results, it is clear that the proposed model outperformed other related approaches, including Greedy Approach, Improved Community-based 316 Robust Influence Maximization (ICRIM), Community Based Influence Maximization Approach (CBIMA), etc. It was also observed from the experiments that most of the propagandistic information is being shared during trending events around the globe, for example, at times of the COVID-19 pandemic.
Business providers are always concerned about maintaining and increasing customer satisfaction and trust because they are crucial for positive word of mouth, revisit intentions, and loyalty, which ...are necessary for long-term success in today's customer-focused industry. This study investigates the dimensions tourists use to evaluate e-service quality and their relationship to E-Loyalty and revisits intention among international tourists visiting Kashmir. The study utilized structural equation modeling (SEM) analysis to investigate whether E-Satisfaction and E-Trust mediate in the understudy relationships. The data was collected from 194 international tourists. The study findings have important implications and guidelines for developing E-Service Quality are discussed.
Urdu is still considered a low-resource language despite being ranked as world's <inline-formula> <tex-math notation="LaTeX">10^{th} </tex-math></inline-formula> most spoken language with nearly 230 ...million speakers. The scarcity of benchmark datasets in low-resource languages has led researchers to utilize more ingenious techniques to curb the issue. One such option widely adopted is to use language translation services to replicate existing datasets from resource-rich languages such as English to low-resource languages, such as Urdu. For most natural language processing tasks, including polarity assessment, words translated via Google translator from one language to another often change the meaning. It results in a polarity shift causing the system's performance degradation, particularly for sentiment classification and emotion detection tasks. This study evaluates the effect of translation on the sentiment classification task from a resource-rich language to a low-resource language. It identifies and enlists words causing polarity shift into five distinct categories. It further finds the correlation between the language with similar roots. Our study shows 2-3 percentage points performance degradation in sentiment classification due to polarity shift as a result of translation from resource-rich languages to low-resource languages.
In smart education, adaptive e-learning systems personalize the educational process by tailoring it to individual learning styles. Traditionally, identifying these styles relies on learners ...completing surveys and questionnaires, which can be tedious and may not reflect their true preferences. Additionally, this approach assumes that learning styles are fixed, leading to a cold-start problem when automatically identifying styles based on e-learning platform behaviors. To address these challenges, we propose a novel approach that annotates unlabeled student feedback using multi-layer topic modeling and implements the Felder–Silverman Learning Style Model (FSLSM) to identify learning styles automatically. Our method involves learners answering four FSLSM-based questions upon logging into the e-learning platform and providing personal information like age, gender, and cognitive characteristics, which are weighted using fuzzy logic. We then analyze learners’ behaviors and activities using web usage mining techniques, classifying their learning sequences into specific styles with an advanced deep learning model. Additionally, we analyze textual feedback using latent Dirichlet allocation (LDA) for sentiment analysis to enhance the learning experience further. The experimental results demonstrate that our approach outperforms existing models in accurately detecting learning styles and improves the overall quality of personalized content delivery.
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