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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. -
Sentiment and emotion analysis using machine learning techniques
Sentiment analysis and text emotion identification have grown in prominence due to their wide range of applications in fields such as psychology, artificial intelligence, human-computer interaction, and so on. There are numerous Machine Learning approaches available for emotion recognition and sentiment analysis. The chapter also delves into the key procedures of data collecting, preprocessing, and emphasising the necessity of good data in training effective models. Real-world applications from a variety of disciplines, including business, healthcare, and entertainment, are investigated to demonstrate the practical utility of these strategies. The chapter also covers the issues of ambiguity, context-awareness, and cross-linguistic disparities, as well as providing suggestions for future study paths. This chapter provides a detailed exploration of machine learning approaches to sentiment and emotion analysis, making it a valuable resource for researchers, practitioners, and students interested in using machine learning to understand and interpret emotional content in textual data. 2025, IGI Global Scientific Publishing. All rights reserved. -
Sentimental analysis on Amazon book reviews: A deep learning approach
[No abstract available] -
Sentimental Analysis on Online Education Using Machine Learning Models
Sentimental analysis is a simple natural language processing technique for classifying and identifying the sentiments and views represented in a source text. Corona pandemic has shifted the focus of education from traditional classrooms to online classes. Students mental and psychological states alter as a result of this transition. Sentimental study of the opinions of online education students can aid in understanding the students learning conditions. During the corona pandemic, only, students enrolled in online classes were surveyed. Only, students who are in college for pre-graduation, graduation, or post-graduation were used in this study. To grasp the pupils feelings, machine learning models were developed. Using the dataset, we were able to identify and visualize the students feelings. Students favorable, negative, and neutral opinions can be successfully classified using machine learning algorithms. The Naive Bayes method is the most accurate method identified. Logistic regression, support vector machine, decision tree, and random forest these algorithms also gave comparatively good accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sentimental analysis on voice using AWS comprehend
Sentimental analysis plays an important role in these days because many start-ups have started with user-driven content [1]. Sentiment analysis is an important research area in natural language processing. Natural language processing has a wide range of applications like voice recognition, machine translation, product review, aspect-oriented product analysis, sentiment analysis and text classification etc [2]. This process will improve the business by analyse the emotions of the conversation. In this project author going to perform sentimental analysis using Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract the content of the document. By using this service can extract the unstructured data like images, voice etc. Thus, will identify the emotions of the conversation and give the output whether the conversation is Positive, Negative, Neutral, or Mixed. To perform this author going to use some services from Aws like s3 which is used for the data store, Transcribe which is used for converting the audio to text, Aws Glue is used to generate the metadata from the comprehend file, Aws Comprehend is used to generate the sentiment file from the audio, Lambda is used to trigger from the data store s3, Aws Athena is used to convert text into structured data and finally there is quick sight where he can visualize the data from the given file. 2020 IEEE. -
SentimentViz: Leveraging RoBERTa in Python for Advanced Sentiment Analysis and Decision-Making for a Famous Indian FMCG (Ayurvedic) Brand
Indian consumer preferences for Ayurvedic brands increasingly turn to the marketplace for well-being. Ayurveda has a deep-rooted history in emerging economies like India, and its increasing role in health, wellness, and exports contributes to Indias economic development. The consumption and changing lifestyle patterns significantly contribute to achieving the United Nations Sustainable Development Goals (SDGs). The primary objective of this study is to explore consumer sentiment that includes perceptions, feelings, and attitudes toward these natural healthcare products contributing to specific SDG targets, leading to good health and well-being. In a data-driven world where governments and businesses seek insights from vast amounts of unstructured text data, sentiment analysis plays a pivotal role in decision-making. Sentiment analysis helps analyze different aspects of unstructured data, including customer experience and insights generated in terms of usage, challenges, and preferences and ultimately helps manage customer engagement. The sentiment analysis requires understanding the context, grouping similar words, removing unrelated content, and then gauging the sentiment of the text. There has always been a challenge to contextualize and gauze the deeper sentiments and create scalable solutions. To build on this need for deeper sentiment understanding and scalable solutions, SentimentViz is a proposed accelerator as part of this paper that leverages Python and chooses the best methodology for text-mining problems. It enables real-time analysis with robust visualization capabilities: In this study, the SentimentViz accelerator is leveraged to estimate the sentiment of 9 products using a robust data science framework and best-of-the-class ML techniques. The detailed consumer sentiment analysis helped to develop a deeper understanding of the value of FMCG (Ayurvedic products) for an emerging economy like India. This will help marketers build targeted marketing campaigns, brand health monitoring, and customer retention strategies through informed decisions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Separate Electorate
In colonial India, the category of Depressed Classes was approached through two distinct lenses as a socio-political issue and a moral and cultural issue. Two towering figures represented these perspectives B. R. Ambedkar and M. K. Gandhi. While Gandhi earnestly sought to address untouchability and other socio-cultural challenges faced by Depressed Classes through moral teachings, his approach was contested for being quite ineffective. Ambedkar, on the contrary, viewed the problem as deeply entrenched within religion and culture, advocating for socio-political empowerment as the solution. This chapter maps the historical events leading to Ambedkars successfully lobbying of British India for a separate electorate for Depressed Classes. It examines Gandhis vehement objection to the same based on his understanding that such provision would be detrimental to Depressed Classes, Hinduism, and national unity. Consequently, this chapter analyses how Ambedkar was forced to come to an agreement with Gandhi and sign the Poona Pact that put an end to proposed separate electorate but granted additional reserved seats to Depressed Classes. This chapter provides historical contexts and discusses the debates around the political representation of the minorities to address the larger question of political share of Depressed Classes in Indian electorate. 2026 selection and editorial matter, Mahitosh Mandal and Sanjiv Kondekar; individual chapters, the contributors. -
Separated/Divorced Individuals Experiences with the Legal System in India: A Qualitative Inquiry
This study identifies systemic flaws and biases in the Indian judicial system, highlighting areas for reform. Through purposive sampling, 15 separated/divorced participants (nine male, six female) were analyzed via semi-structured interviews and Interpretative Phenomenological Analysis (IPA) using Atlas.ti 23 software. Analysis derived one group experiential theme: Judicial Process and Law Enforcement, with four sub-themes: The Contradictory Positions of the Court, Nonchalance toward False Accusations, Encounter with Legal Professionals, and Two Sides of Law Enforcement. Participants revealed conflicting experiences, with few achieving justice while others faced substantial delays. Male participants often encountered allegations without evidence. Regardless of gender, feelings of distress and helplessness were prevalent due to court procedures. The findings highlight the urgent need for reforms like procedural transparency to mitigate the trauma of divorce in India and emphasize a gap in existing literature on judicial effects in separation cases. Recommendations for future research are suggested. 2025 Taylor & Francis Group, LLC. -
Serendipitous detection of an intense X-ray flare in the weak-line T Tauri star KM Ori with SRG/eROSITA
Weak-line T Tauri stars (WTTS) exhibit X-ray flares, likely resulting from magnetic reconnection that heats the stellar plasma to very high temperatures. These flares are difficult to identify through targeted observations. Here, we report the serendipitous detection of the brightest X-ray flaring state of the WTTS KM Ori in the eROSITA DR1 survey. Observations from SRG/eROSITA, Chandra X-ray Observatory, and XMM-Newton are analysed to assess the X-ray properties of KM Ori, thereby establishing its flaring state at the eROSITA epoch. The long-term (1999-2020) X-ray light curve generated for the Chandra observations confirmed that eROSITA captured the source at its highest X-ray flaring state recorded to date. Multi-instrument observations support the X-ray flaring state of the source, with time-averaged X-ray luminosity reaching at the eROSITA epoch, marking it the brightest and possibly the longest flare observed so far. Such intense X-ray flares have been detected only in a few WTTS. The X-ray spectral analysis unveils the presence of multiple thermal plasma components at all epochs. The notably high luminosity , energy (erg), and the elevated emission measures of the thermal components in the eROSITA epoch indicate a superflare/megaflare state of KM Ori. Additionally, the H line equivalent width of from our optical spectral analysis, combined with the lack of infrared excess in the spectral energy distribution, were used to re-confirm the WTTS (thin disc/disc-less) classification of the source. The long-duration flare of KM Ori observed by eROSITA indicates the possibility of a slow-rise top-flat flare. The detection demonstrates the potential of eROSITA to uncover such rare, transient events, thereby providing new insights into the X-ray activity of WTTS. The Author(s), 2025. Published by Cambridge University Press on behalf of Astronomical Society of Australia. -
Serendipitous detection of an intense X-ray flare in the weak-line T Tauri star KM Ori with SRG/eROSITA
Weak-line T Tauri stars (WTTS) exhibit X-ray flares, likely resulting from magnetic reconnection that heats the stellar plasma to very high temperatures. These flares are difficult to identify through targeted observations. Here, we report the serendipitous detection of the brightest X-ray flaring state of the WTTS KM Ori in the eROSITA DR1 survey. Observations from SRG/eROSITA, Chandra X-ray Observatory, and XMM-Newton are analysed to assess the X-ray properties of KM Ori, thereby establishing its flaring state at the eROSITA epoch. The long-term (1999-2020) X-ray light curve generated for the Chandra observations confirmed that eROSITA captured the source at its highest X-ray flaring state recorded to date. Multi-instrument observations support the X-ray flaring state of the source, with time-averaged X-ray luminosity reaching at the eROSITA epoch, marking it the brightest and possibly the longest flare observed so far. Such intense X-ray flares have been detected only in a few WTTS. The X-ray spectral analysis unveils the presence of multiple thermal plasma components at all epochs. The notably high luminosity , energy (erg), and the elevated emission measures of the thermal components in the eROSITA epoch indicate a superflare/megaflare state of KM Ori. Additionally, the H line equivalent width of from our optical spectral analysis, combined with the lack of infrared excess in the spectral energy distribution, were used to re-confirm the WTTS (thin disc/disc-less) classification of the source. The long-duration flare of KM Ori observed by eROSITA indicates the possibility of a slow-rise top-flat flare. The detection demonstrates the potential of eROSITA to uncover such rare, transient events, thereby providing new insights into the X-ray activity of WTTS. The Author(s), 2025. Published by Cambridge University Press on behalf of Astronomical Society of Australia. -
Series Preface
[No abstract available] -
Series solutions for an unsteady flow and heat transfer of a rotating dusty fluid with radiation effect
A theoretical analysis of free convective MHD flow of an unsteady rotating dusty fluid under the influence of hall current and radiation effect is carried out. The fluid flow is considered in the porous media under the influence of periodic pressure gradient and the fluid is assumed to be viscous, incompressible and electrically conducting with uniform distribution of dust particles. The governing partial differential equations are solved analytically using perturbation technique and the expressions for skin-friction is also derived. Further the effect of various pertinent parameter like magnetic parameter, rotation parameter and Hall current parameter on velocity of both fluid and dust phases are depicted graphically and the effect of radiation parameter, Grashof number and Prandtl number on temperature profile is also discussed in detail. 2017, Univerzita Komenskeho. All rights reserved. -
Servant leadership and diversity: A focus on ethnic and cultural diversity
Business organizations becoming global is nothing new in the current world due to the ever-shrinking physical and communication boundaries. Going global has its benefits and limitations. Benefits would be less expensive land, labor, and resources and reduced transportation cost by being present in countries with vast requirements for an organization's products or services. At the same time, the limitations would be to manage people or lead them toward shared organizational goals. India being a country with enormous opportunities has diverse cultures and practices. Thus, leading various people as employees would be a challenge. What may work in the Western countries may not work in India due to its vast diversity in culture, language, and ethnicity. This research aims to understand the servant leadership approach and if it would be applicable in India. In the context of diverse cultures, the authors analyze the servant leader's role in an organization and compare the practices of servant leadership in various other countries. 2023 by IGI Global. All rights reserved. -
Servant Teachers and Online Learning in Higher Education: A Narrative Enquiry into Experiences of Teachers During COVID-19 Outbreak
This research examines the lived experiences of servant teachers of higher education during the COVID-19 pandemic focusing on the use of technology and challenges within online teaching frameworks. It aims to fill the gaps in literature regarding the individual practitioner-servant leadership in digital education settings. Ethical considerations were rigorously maintained. This research adapted qualitative research approaches guided by the SL-7 scale to identify servant teachers and semi-structured interviews to capture their lived experiences. Data analysis employed interpretive phenomenological analysis (IPA) through Osborn and Smith's (2006) four stages of IPA. The analysis yielded seven core themes: engagement, technology, experiences, impact, well-being, performance, and policies. The study illuminates how pedagogical approaches need to be student-centered but were technologically constrained from a servant teacher's perspective, thus shedding light on the dynamics of servant leadership on supportive and nurturing pedagogies in education during emergencies. The research contributes to the discourse on leadership in digital education by emphasizing the value of servant leadership traits in enhancing student satisfaction, retention, and overall academic well-being. 2026 by Divya Dosaya, Anupama Sadasivan, Sonia David, Athira M. and Palak Pipalia. -
Serverless Architecture - A Revolution in Cloud Computing
Emergence of cloud computing as the inevitable IT computing paradigm, the perception of the compute reference model and building of services has evolved into new dimensions. Serverless computing is an execution model in which the cloud service provider dynamically manages the allocation of compute resources of the server. The consumer is billed for the actual volume of resources consumed by them, instead paying for the pre-purchased units of compute capacity. This model evolved as a way to achieve optimum cost, minimum configuration overheads, and increases the application's ability to scale in the cloud. The prospective of the serverless compute model is well conceived by the major cloud service providers and reflected in the adoption of serverless computing paradigm. This review paper presents a comprehensive study on serverless computing architecture and also extends an experimentation of the working principle of serverless computing reference model adapted by AWS Lambda. The various research avenues in serverless computing are identified and presented. 2018 IEEE. -
Serverless Data Processing System and its Design Space Consideration
Serverless computing is becoming increasingly important in data-processing applications in science and business. The scheduler is at the centre of serverless data-processing systems, allowing for dynamic decisions on job and data placement. The complex design space, which is influenced by various user, cluster, and workload variables, presents problems for developing high-performance and cost-effective scheduling structures and processes. To make this exploration easier, we present Sched-Probe, a framework that includes a conceptual model and simulator for systematic design space exploration. Using the Sched-Probe framework, we evaluate the performance of three scheduling systems and two techniques using real-world workloads. Our open-source software is now available on ExDe, allowing system designers to collaborate on delving into the complexity of serverless scheduling, paving the way for optimised and efficient data-processing systems. 2024, Iquz Galaxy Publisher. All rights reserved. -
Service delivery quality improvement models: A review /
Procedia Social Science And Behavioral Sciences, Vol.144, pp.510-527, ISSN No: 1877-0428. -
Service industry alchemy: A symphony of digital innovations in customer engagement
The emergence of digitization, automation, and artificial intelligence has transformed service delivery, allowing businesses to increase productivity, tailor client experiences, and provide cutting-edge solutions. The delivery, use, and accessibility of services are changing in various service sectors due to innovations. Among them, healthcare, education, and finance have received considerable attention in recent years. To synthesize prior research on innovations in the service industry, the chapter attempts a thematic, sentiment, and bibliometric analysis of the research domain. For the analysis, data was extracted from the Scopus database and was filtered by application of inclusion-exclusion, with the use of NVivo and Bibliometric software VOS viewer. Most productive and influential articles, authors, journals, and affiliations were recognized. Thematic mapping and trend analysis revealed past and present research subdomains that were used for the prediction of future research agendas. 2024, IGI Global. All rights reserved. -
Service learning as a pedagogical strategy: A case study on disability inclusion
Diversity and inclusion are the growing concerns of society and every organisation including higher educational institutions (HEIs) is designing strategies to ensure diversity equality and inclusion (DEI). To understand how students engage with the idea of disability and inclusion in education, this study explores the journey of undergraduate students with an organization for visually challenged students. This qualitative, descriptive study used interviews and focus group discussion (FGD) along with the analysis of chosen reflective journals. SL activities enabled students to reflect on the learning challenges faced by the visually challenged students as well as their caretakers. This also made them reflect on the larger academic environment at the higher education level and come up with suggestions to make the HEIs more inclusive for the visually challenged. This study emphasizes the nature of their engagement and the transformative aspects of the learning process that they experienced by focusing on the students' experiences and challenges. 2024, IGI Global. All rights reserved. -
Service learning beyond the classroom: Engaging students in environmental stewardship
Service learning offers a dynamic approach to education, extending learning beyond the classroom and into the community. This chapter explores integrating service learning principles with environmental stewardship, focusing on engaging students in meaningful activities that contribute to the conservation and protection of the environment. By immersing students in real-world environmental projects, this chapter integrates six stages of the IPARDC model with environmental stewardship. Besides that, this chapter discusses a six-step system focused on environmental stewardship for service learning. Moreover, it discusses strategies for designing and implementing effective service-learning experiences that promote environmental awareness, sustainability, and social responsibility. Through collaboration with community partners and integration with the academic curriculum, service learning empowers students to actively participate in environmental stewardship efforts actively, fostering a sense of responsibility and advocacy for the natural world. 2024, IGI Global. All rights reserved.