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Sentiment and Emotion Analysis of Significant Diseases in India and Russia
Healthcare organizations need this information to understand and treat the patient's concerns. The motivation for this kind of analysis is how patients provide this information while wrapping it in their thoughts and emotions. It is less practicable to manually study all the free and abundant health-related knowledge accessible online to arrive at decisions that might contribute to an immediate and beneficial decision. Sentiment analysis methods perform this function through automated procedures with minimal human intervention. In this paper, an investigation is conducted to compare the region-wise, language-wise, and sentiment analysis of the tweets collected from Russia and India. The results obtained through research have shown some significant characteristics of the language models used for language detection. The inferenc and analysis obtained from the observations are included in this paper. 2023 IEEE. -
Intelligent Approaches of Clinical and Nonclinical Type-1 Diabetes Data Clustering and Analysis
Every year in India, there are nearly 15,600 fresh cases being reported among these age groups. In 2011, in the United States, 18,000 children under 15 were newly reported for T1DM. Over 13years, the Karnataka state government has a list of records showing that out of 100,000, 37% of boys and 40% of girls are affected by T1DM Disease. This paper investigates two methodologies to identify significant details about Type-1 diabetes. The first methodology is applicable to clinical data. The second methodology is demonstrated for the NDA T1D dataset. The dataset is utilized further to apply machine learning techniques to group similar patient traits. Exploratory data analysis on the dataset has revealed significant information answering a few research questions. This analysis can be useful for India, China, and other countries with high populations. In this paper, a unique methodology based on Artificial Intelligence Technique is proposed for both clinical and non-clinical data. The Autoimmune Disease, Diabetes Type 1-T1D, is focused. Non Clinical data based on 2021 reports are collected to identify patterns. Substantial unique issues are addressed in this work which were never reported before. The knowledge generated can be helpful for creating new clinical datasets, methodology and new insights related to Type-1 diabetes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An empirical analysis of similarity measures for unstructured data
With fast growth in size of digital text documents over internet and digital repositories, the pools of digital document is piling up day by day. Due to this digital revolution and growth, an efficient and effective technique is required to handle such an enormous amount of data. It is extremely important to understand the documents properly to mine them. To find coherence among documents text similarity measurement pays a humongous role. The goal of similarity computation is to identify cohesion among text documents and to make the text ready for the required applications such as document organization, plagiarism detection, query matching etc. This task is one of the most fundamental task in the area of information retrieval, information extraction, document organization, plagiarism detection and text mining problems. But effectiveness of document clustering is highly dependent on this task. In this paper four similarity measures are implemented and their descriptive statistics is compared. The results are found to be satisfactory. Graphs are drawn for visualization of results. 2019 COMPUSOFT, An international journal of advanced computer technology. -
Discovering patterns using feature selection techniques and correlation
Term Frequency and inverse document frequency is reported to have a significant contribution for various text categorization, document clustering and many other text mining related tasks. A collection of the applications and the enhancements of the Term Frequency and Inverse Document Frequency based document representation technique is examined in this work. The document representation algorithm is essential in the field of text - script mining. In this algorithm, unstructured data is converted into a vector space model where each related document is considered as a point in the vector space. Related documents come in proximity to the other related documents while the documents that are very far away from being coherent remain different from each other. In this paper, four feature selection techniques are implemented to discover the patterns from a repository of unstructured data by using correlation similarity measure. Analysis and comparison with other existing technique is also included. The validation of the patterns formed is performed by using silhouette values. Experiments are conducted to compare performance. Results indicate that TDMp1 performance is poor compared to others. Springer Nature Switzerland AG 2020. -
Evaluation of ML-Based Sentiment Analysis Techniques with Stochastic Gradient Descent and Logistic Regression
In recent times, along with the expansion of technology, the Internet also has flourished exponentially. World is more connected today not only through the technology, but also through sharing sentiments to express views, either be constructive or destructive in front of the world through social media. Twitter, Facebook, Instagram, etc., are being used as social media to reach the world. The study of understanding peoples emotions, intentions, attitudes from unstructured data is opinion mining/sentiment analysis. This is an application of NLP or text mining. In this paper, an attempt is made to realize sentiment analysis's multiple dimensions using approaches such as ML and NLP-based technqies like word frequency and TF-IDF. Using ML approach, experiments were conducted, and the performance of the predictions was visualized. Three different datasets are used. A comparison of logistic regression (LR) and stochastic gradient descent (SGD) algorithms are compared using two different document representation. An extensive comparison is carried out using three different types of dataset. Amazon instant video datasets, bank dataset and movie reviews datasets are being used for the same. Analysis of performance is accomplished by using different graphs. The results indicate that logistic regression performs better than stochastic gradient descent for movie review dataset by using word frequency and TF-IDF-based approach. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Comparative Analysis of Sentiment Analysis Using RNN-LSTM and Logistic Regression
Social media analytics makes a big difference in the success or failure of an organization. The data gathered from social media can be used to get a hit type product by analyzing the data and getting important information about the need of the people. This can be done by implementing sentiment analysis on the available data and then accessing the feelings of the customers about the product or service and knowing if it is actually being liked by them or not. Tracking data of the customers helps the organization in many ways. This study was done to get familiarized with the concept of data analytics and how social media plays an important role in it. Furthermore, Web scraping of Twitter and YouTube data was done following which a standard dataset was selected to do the other analytics. The field of sentiment analysis was used to get the emotions of the people. Logistic regression and RNN-LSTM models were used to perform the same, and then, the results were compared. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A comparative analysis of opinions and sentiments on clean India campaign and sustainability goals of 2030
Human are blessed with natural intelligence. Artificial Intelligence can help human minds to make a best usage of machines to handle huge amount of data with accuracy and precision. AI has a widespread application in 21st century. Opinion mining is an application of artificial intelligence. The opinions expressed in social media can be extracted using python which can be used as an input for various machine learning algorithms to identify many patterns which can help policy makers to make effective policies. Clean India Campaign started in India with a set of goals to be achieved. Sustainability goals of 2030 given by United Nations puts light on many important aspects which need immediate attention in the next 9 years. Current pandemic Covid-19 has also triggered the necessity behind putting immediate attention for a better tomorrow. Without proper awareness programs, brainstorming knowledge cultivation, orienting minds towards the "what-why-where"aspects of sustainable growth in each sphere of life, aligning industrial development and digital era towards sustainable industrial development in digital era, sustainable economy, sustainable care of each natural resource; it is not easy to accomplish the sustainability goals of 2030 given by United Nations.This work emphasizes on the case study conducted as an initiative to motivate future policy makers to be aware of the different dimension of 2030 United Nations Agenda and the clean India campaign to take initiatives as a professional through the skills learned focusing on India. Realizing Individual social Responsibility can make a big difference in the planning and implementation of the goals and missions. Swachch Bharat Abhiyan (Clean India Campaign) started Swachch Bharat Mission-Urban (SBM-U) with a few objectives to make India Clean.This work has proposed two phases for analyzing opinions. This research have provided a methodology to apply AI to improve the opinion mining. The conventional opinion analysis is limited by reachability but the automated opinion analysis can be scaled up using artificial intelligence based applications. The uniqueness of the work lies in its focus on 'one-three verticals' in phase 1 of the methodology. Many prominent regions of India are considered as a part of the study. It helps us to provide a clearer picture across different regions of India. It also provide an avenue to list tasks to be done for each region and a set of ways which could be adopted by the future professionals and current stakeholders of higher education institute. Phase 2 focusses on more number of opinions collected from across the globe through digital platforms. 2021 Author(s). -
Performance Analysis of Logistic Regression, KNN, SVM, Nae Bayes Classifier for Healthcare Application During COVID-19
Heart disease is one of the main causes of mortality in India and the USA. According to statistics, a person dies out of a heart-related disease every 36s. COVID-19 has introduced several problems that have intensified the issue, resulting in increased deaths associated to heart disease and diabetes. The entire world is searching for new technology to address thesechallenges. Artificial intelligence [AI] and machine learning [ML] are considered as the technologies, which are capable of implementing a remarkable change in the lives of common people. Health care is the domain, which is expected to get the desirable benefit to implement a positive change in the lives of common people and the society at large. Previous pandemics have given enough evidence for the utilization of AI-ML algorithm as an effective tool to fight against and control the pandemic. The present epidemic, which is caused by Sars-Cov-2, has created several challenges that necessitate the rapid use of cutting-edge technology and healthcare domain expertise in order to save lives. AI-ML is used for various tasks during pandemic like tracing contacts, managing healthcare-related emergencies, automatic bed allocation, recommending nearby hospitals, recommending vaccine centers nearby, drug-related information sharing, recommending locations by utilizing their mobile location. Prediction techniques are used to save lives as early detections help to save lives. One of the problems that might make a person suffering from COVID-19 extremely sick is heart disease. In this research, four distinct machine learning algorithms are used to try to detect heart disease earlier. Many lives can be saved if heart disease can be predicted earlier. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Towards sustainable business: Review of sentiment analysis to promote business and well-being
Sustainability in business is expected considering the growth in the long run. Sustainable development goals are important for our sustainability on this planet. In case of a business, it is essential to ensure sustainable processes and sustainability of the existing customers. Sustainable customers can in turn contribute to improving the process by providing constructive suggestions to the business. This paper is an attempt to review sentiment analysis techniques to improve the customer experience of a business. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Exploring Sustainable and affordable Cancer Care using Artificial Intelligence
Now, in recent decades AI and ML have become a major part in developing and maintaining the healthcare system. Now, by using AI and ML in healthcare, it can provide a massive help for the healthcare workers.AI and ML help the healthcare workers for making better decisions, In some practical areas, it may take the place of human action for making decisions such as radiology, it can help to Gather medical knowledge or information from different journals, textbooks, or clinics which will help in reducing time for study and research. AI and ML help in predicting the early diagnosis of disease based on the patient's data and even help to prevent that dis-ease. Breast cancer is the most frequent category with an estimation of 2, 38,908 by 2025. Breast cancer is followed by lung cancer (1, 11,328); followed by mouth cancer (90,060). These statistics have triggered this research. Breast cancer is found in every one women among eight woman. Sustainable care shall help to fight with the disease. Sustainable care includes affordable cancer care and it's possible through early prediction of cancer. In this research we are using artificial intelligence based techniques for early prediction of cancer. Future direction of work will focus on usage of transfer learning and other models of AI-ML to help the society and mothers of nations to fight against the in-creasing spread of cancers. The Electrochemical Society -
In situ fabricated MOF-cellulose composite as an advanced ROS deactivator-convertor: Fluoroswitchable bi-phasic tweezers for free chlorine detoxification and size-exclusive catalytic insertion of aqueous H2O2
Combining the merits of structural diversity, and purposeful implantation of task-specific functionalities, metal-organic frameworks (MOFs) instigate targeted reactive oxygen species (ROS) scavenging and concurrent detoxification via self-calibrated emission modulation. Then again, grafting of catalytically active sites in MOFs can benefit developing a greener protocol to convert ROS generators to technologically important building blocks, wherein tailorable MOF-composite fabrication is highly sought for practical applications, yet unexplored. The chemo-robust and hydrogen-bonded framework encompassing free -NH2 moiety affixed pores serves as an ultra-fast and highly regenerable fluoro-probe for selective detection of toxic ROS producers hypochlorite ion (ClO-) and H2O2 with record-level nanomolar sensitivity. While the bio-relevant antioxidant l-ascorbic acid (AA) imparts notable quenching to the MOF, a significant 3.5 fold emission enhancement with bi-phasic colorimetric variation ensues when it selectively scavenges ClO- from uni-directional porous channels through an unprecedented molecular tweezer approach. Apart from a battery of experimental evidence, density functional theory (DFT) results validate "on-off-on"fluoroswitching from redistribution of MOF orbital energy levels, and show guest-mediated exclusive transition from "Tight state"to "Loose state". The coordination frustrated metal site engineered pore-wall benefits the dual-functionalized MOF in converting the potential ROS generator H2O2via selective alkene epoxidation under mild-conditions. Importantly, sterically encumbered substrates exhibit poor conversion and demonstrate first-ever pore-fitting-induced size selectivity for this benign oxidation. Judiciously planned control experiments in combination with DFT-optimized intermediates provide proof-of-concept to the ionic route of ROS conversion. Considering an effective way to broaden the advanced applications of this crystalline material, reconfigurable MOF@cotton fiber (CF) is fabricated via in situ growth, which scavenges free chlorine and concomitantly squeezes it upon exposure to AA with obvious colorimetric changes over multiple real-life platforms. Furthermore, multi-cyclic alkene epoxidation by MOF@CF paves the way to futuristic continuous flow reactors that truly serves this smart composite as a bimodal ROS deactivator-convertor and explicitly denotes it as an advanced promising analogue from contemporary state-of-the-art materials. The Royal Society of Chemistry. -
Real-Time Football Match Analysis with Region-Independent Player Tracking Using Deep Learning
This project explores the application of AI in analyzing football games by tracking players across the entire video frame. Unlike traditional methods that focus on limited areas, the system here uses YOLO for detecting players everywhere in the frame and ByteTrack to follow them throughout the match. The goal is to get a clearer picture of each player's movement, particularly their speed and distance covered. Manual methods or GPS-based tools often fall short in providing quick, reliable data, especially in real-time scenarios. This study compensates for camera motion and adjusts for different viewpoints to get more accurate tracking results. As a way to test player identity consistency, the system randomly assigns popular player names to different tracking IDs. Experiments on public match videos show that the system can keep track of players even during zoom-ins, crowding, or partial visibility. Code snippets show how the model works in practice. Our results show that using full-frame AI tracking gives coaches more detailed tactical insights and helps them develop more effective strategies. 2025 IEEE. -
Air quality index improvement through machine learning and quantum computing: a framework for advancing air quality prediction using quantum-inspired metaheuristics on climate change to achieve positive health
Climate change significantly exacerbates air quality deterioration, intensifying health risks and environmental instability. Air pollution poses significant challenges to public health and environmental sustainability. Accurate prediction of the Air Quality Index (AQI) is crucial for timely interventions and policy-making. As urbanization and industrial activities intensify, there is an urgent need for accurate and real-time air quality monitoring systems. Advanced machine learning (ML) techniques have shown promise in air quality forecasting and classification. Recently, quantum-inspired computational paradigms have emerged as innovative tools to overcome the limitations of traditional models, particularly in areas like feature selection, optimization, and spatial-temporal pattern recognition. This study presents a comprehensive analysis of various machine learning and deep learning models for AQI prediction, utilizing pollutant concentration data. It also explores quantum computing-inspired approaches. We explore the efficacy of different algorithms, datasets, and preprocessing techniques. This paper critically reviews high-impact research that explores the intersection of climate-induced changes and air quality prediction using ML. It identifies trends, gaps, and emerging methodologies. We conduct a comparative analysis of datasets, prediction models, and performance metrics. The paper focuses on three case studies. The first case study focuses on the Indian aspect using an Indian dataset and the global aspects with different global datasets, and the second case study uses quantum-inspired approaches. We further evaluate the performance of 10 state-of-the-art ML models, offering a roadmap for future research and deployment. Effective air quality forecasting is vital in urban planning decisions. This also plays an essential role need in environmental management and the protection of public health. This issue directly deals with Sustainable Development Goal (SDG) 3 and SDG 13. SDG 3 is related to positive health and SDG 13 is related to climate action. Conventional predictive models in ML face challenges due to multiple reasons. Effective feature selection is one such challenge as well as effective hyperparameter tuning. These challenges limit the effectiveness of artificial intelligence models. In the proposed framework, searching is enhanced using quantum jump- and quantum mechanics-related principles. This approach leads to the development of a quantum-inspired particle swarm optimization called QPSO. QPSO is able to provide more promising results by bridging the gaps of traditional optimization techniques. Model convergence is accelerated by using quantum-inspired feature selection techniques. 2026 Elsevier Inc. All rights reserved. -
Decoding sustainability: A machine learning-based analysis of socioeconomic drivers in global sustainable developmental goals progress
Sustainability, a concept that gained prominence with the Brundtland Report in 1987, is defined as a development approach that addresses present needs without jeopardizing the ability of future generations to meet theirs. Over the years, sustainability has evolved beyond its initial environmental focus, now encompassing economic, social, and political dimensionsmaking it an essential pillar of modern development initiatives. To drive global sustainable development forward, the United Nations adopted the 2030 Agenda, featuring 17 Sustainable Development Goals (SDGs). These goals aim to resolve some of the most pressing challenges faced by humanity, including poverty eradication, climate action, gender equality, and economic growth. The SDG Index, which evaluates a countrys progress toward these goals, helps measure and compare performance across nations. The Intersection of Socioeconomic Factors and SDG Progress is significant for the growth of a country. A countrys Gross Domestic Product (GDP) has often been seen as a key economic indicator, reflecting its ability to invest in sustainable initiatives. However, sustainability is not solely dependent on financial resourcessocial factors play a critical role. To assess the connection between well-being and sustainability, researchers often analyze the Happiness Index alongside SDG scores. Countries demonstrating both high happiness levels and strong sustainability scores provide valuable insights into the relationship between social welfare and global progress. Furthermore, machine learning (ML) techniques have emerged as powerful tools in sustainability research. By analyzing vast datasets, AI-driven approaches can predict trends, optimize resources, and enhance policy implementationaccelerating progress toward a sustainable future. The Evolving Landscape of Sustainability and Its Global Impact is realized using statistical and ML approaches in this study. Rethinking Strategies for a Sustainable Tomorrow is very important in 2025 as we are approaching 2030 very fast. Understanding the underlying factors influencing SDG scores allows nations to refine their approaches to sustainability. By tailoring action plans based on socioeconomic conditions, governments can improve their policies, ensuring both environmental stewardship and enhanced quality of life for their citizens. As global challenges evolve, interdisciplinary approachesspanning technology, economics, and social scienceswill continue to shape sustainability efforts, fostering a future where development aligns seamlessly with environmental and societal well-being. 2026 selection and editorial matter, Siddhartha Bhattacharyya, Jan Plato, Soumyadip Dhar, Naba Kumar Mondal, Ivan Zelinka, Jyoti Sekhar Banerjee and Abhijit Das; individual chapters, the contributors. -
State-of-the-Art and Upcoming Trends in IoT-Enabled Smart Cities
Modern cities tremendous development of urbanization necessitates smart responses to pressing problems like mobility, medical care, power, and civil construction. The Internet of Things (IoT), which can use sustainable data and communication innovations, is evolving into the foundation for the upcoming trends of smart cities. To meet the demands of the expanding populace, several demands of the smart city must be taken into account. The IoT expansion has greatly generated a variety of study avenues for the smart city on the flip side of developing innovation. The suggested research proposal offers the analytic network procedure (ANP) for analyzing smart cities while maintaining in mind application instances of the smart city. In complicated circumstances when there are ambiguous options, the ANP technique performs effectively. The projected methods experimental findings demonstrate its viability for use case-based evaluation of IoT-enabled smart cities. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Machine Learning Technique to Detect Radiations in the Brain
The brain of humans and other organisms is affected in various ways through the electromagnetic field (EMF) radiations generated by mobile phones and cell phone towers. Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF. Cellular level analysis is used to measure and detect the effect of mobile radiations, but its utilization seems very expensive, and it is a tedious process, where its analysis requires the preparation of cell suspension. In this regard, this research article proposes optimal broadcasting learning to detect changes in brain morphology due to the revelation of EMF. Here, Drosophila melanogaster acts as a specimen under the revelation of EMF. Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF. The geometrical characteristics of the brain image of that is microscopic segmented are analyzed. Analysis results reveal the occurrence of several prejudicial characteristics that can be processed by machine learning techniques. The important prejudicial characteristics are given to four varieties of classifiers such as nae Bayes, artificial neural network, support vector machine, and unsystematic forest for the classification of open or nonopen microscopic image of D. melanogaster brain. The results are attained through various experimental evaluations, and the said classifiers perform well by achieving 96.44% using the prejudicial characteristics chosen by the feature selection method. The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity, where the machine learning techniques produce an effective framework for image processing. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. -
The psychosocial impact of and access to mental health care for individuals with visual impairments
Blindness and Visual Impairment is one of the first recognized disabilities out of the 21 disabilities delineated by the Rights for Persons with Disabilities Act. Despite its recognition, a large research gap was identified regarding access to mental health care in India. Purpose: Further, the distinction between impairment and disability characterized by differentiating the medical impact and psychosocial impact was not widely explored in existing literature. Methods and materials: This qualitative phenomenological research explored the lived experiences of 11 adults with visual impairments in India, examining psychosocial challenges and mental healthcare access. Results: Four Key themes were derived with the use of the Braun and Clarke model of thematic analysisImpact of Visual Impairment, Coping Strategies, Perceived Support and Access to Mental Healthcare. Conclusion: The findings revealed unique experiences and influences on wellbeing and daily functioning as well as the role stigma plays in perpetuating contemporary disability rhetoric. The study emphasizes the need for tailored policies and interventions to address the psychosocial and mental healthcare challenges faced by individuals with visual impairments. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
A Novel Real-Time Posture Monitoring System Using Signal Processing and Computer Vision Techniques
This paper presents a novel real-time posture monitoring system using signal processing and computer vision techniques to provide accurate feedback on body posture. By measuring key angles between the head-shoulder and shoulder-hip regions, the system identifies deviations from ideal posture. A Butterworth low-pass filter is employed to smooth the posture data, significantly reducing noise and misclassification of sudden movements as poor posture. The proposed systems novelty lies in the integration of signal processing to enhance data interpretation, ensuring that momentary shifts are filtered out, resulting in more reliable classification and feedback. The system was tested in real-world scenarios, demonstrating its ability to offer immediate, high-accuracy posture feedback. Unlike conventional systems that rely solely on raw data, our approach uses smoothed, noise-free data to provide a clearer understanding of posture, making it suitable for deployment in workplaces, home offices, and rehabilitation centers. Future work will focus on multi-joint analysis, duration-based feedback mechanisms for sustained posture deviations, and the impact of camera angle on measurement accuracy. Overall, the system provides a cost-effective and efficient solution for continuous posture monitoring, aiming to improve health and ergonomics across various settings. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Examining the impact of maternal experiences of domestic violence on the mental health of their adolescent children in India
Background Domestic violence (DV) is experienced by one in three women in India and is linked to poor mental health outcomes. We hypothesize that maternal experiences of DV can have negative impacts on the mental health of their children. Previous studies have demonstrated this link in Western countries, however culturally specific manifestations of DV and mental health disorders and socio-cultural differences in parent-child relationships and home environments necessitate deeper understanding of the impacts of maternal experiences of DV on children in the Indian context. Methods This study presents a secondary analysis of data collected from a seven-center study in urban and rural India examining mental health disorders among adolescents aged 1217 years and psychological, physical, and sexual abuse affecting their mothers. The Indian Family Violence and Control Scale (IFVCS) was used to examine experiences of DV among mothers and the Mini International Neuropsychiatric InterviewKid (MINI-Kid) was used to examine mental health outcomes among adolescents. Multivariate analyses examined the associations between maternal DV and adolescent mental disorders. Results Data from 2,784 adolescent-mother pairs were analyzed. In bivariate analyses, maternal experiences of physical, psychological, and sexual abuse were significantly associated with adolescent common mental disorders including anxiety and depression (p < 0.05). After adjusting for adolescent gender, site, and education status in the multivariate analysis, physical, sexual, and any DV were significantly associated with adolescent anxiety disorders and common mental disorders. Physical abuse was significantly associated with adolescent depressive disorders. Conclusions These results suggest that exposure to maternal DV significantly impacts adolescent mental health in India and underscore the need to develop trauma-informed school programs and enhance DV prevention for women in India. 2025 Gourisankar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. -
Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies
The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%. 2022 by the authors. Licensee MDPI, Basel, Switzerland.