Language Support Research Articles

Building Research Capacity and Promoting Collaboration Through Grant Writing: Lessons From Ukrainian Universities

<p style="text-align: justify;">This study examines the state of grant writing practices in Ukrainian universities and proposes recommendations to enhance their capacity to secure external funding for research and development. Through a mixed-methods approach involving surveys and semi-structured interviews, data was gathered from researchers and faculty members. The study identifies challenges faced by Ukrainian universities in obtaining external funding and highlights important themes for improving grant-writing practices. These themes include promoting collaboration, providing resources and infrastructure, implementing language support policies, raising awareness of funding opportunities, offering institutional support, fostering a research culture, establishing internal grant review processes, leveraging international collaborations, and emphasising evaluation and learning. By addressing these themes, Ukrainian universities can improve their grant-writing practices, increase proposal competitiveness, and enhance success rates in securing external funding. This study provides valuable insights, complements existing literature, and offers practical recommendations for grant writing in Ukrainian universities.</p>

Databricks- Data Intelligence Platform for Advanced Data Architecture

Databricks, as a unified analytics platform, has emerged at the forefront of this evolution, offering scalable cloud-based solutions for data science and ML applications. This article explores the journey of Databricks in enabling data-driven decision-making through advanced analytics techniques. From its roots in Apache Spark to its current status as a leading platform for data engineering, data science, and machine learning, Databricks has continuously evolved to meet the growing demands of modern enterprises. This article examines the progression of data science/Machine Learning applications in Databricks, tracing their development from initial implementation to current state-of-the-art techniques and integration within the platform. Initially, the article delineates the inception of Databricks, focusing on its architecture and the early adoption of Apache Spark for big data processing. It explores how the platform's native support for various programming languages and its unified analytics engine facilitated the early stages of intelligent application development. The article further discusses the implications of these advancements for the future of data science and Intelligence within Databricks and the broader analytics ecosystem. It highlights the potential for further integration of AI and ML technologies, such as automated machine learning (AutoML) and real-time analytics, in enhancing decision-making processes and operational efficiencies across industries. The evolution of data science in Databricks has played a pivotal role in advancing big data analytics, offering scalable, efficient, and user-friendly solutions. This study not only charts the historical development of these applications within Databricks but also provides insights into future trends and potential areas for innovation. As data continues to grow in volume and complexity, platforms like Databricks will be instrumental in harnessing the power of data science and ML to drive insights and value across sectors.

“Dime con Quién Andas y te Diré Qué Piensas Sobre el Español de los US”: Language Attitudes and Motivation to Learn Spanish as a Heritage Language through the Lens of Social Networks

This study explores the motivation and attitudes of heritage speakers (HSs) of Spanish, focusing on the influence of their social networks. Previous research highlighted variations in HS motivation, attributed to social, cultural, and contextual factors. The study investigates how HS communities shape motivation and attitudes towards learning the heritage language (HL). Employing personal network analysis, the research surveyed 26 Spanish HSs in a Spanish heritage language program. Results revealed that HS networks primarily consisted of emotionally close family members. Positive and negative factors within these networks, such as language support, confidence, shame, and expectations, significantly influenced HS motivation and attitudes. Language attitudes within the network positively impacted individual attitudes, indicating a process of internalizing shared values. The study emphasizes the importance of considering the context surrounding HSs and suggests that addressing language expectations and fostering language support in communities may positively transform perceptions of Spanish in the United States. The findings underscore the effectiveness of a personal network approach in recreating the external environment beyond the language classroom.

Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults

Background: The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand. Methods: A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims. Results: The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes. Discussion: This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions.

Linguistically responsive formative assessment for emergent bilinguals: exploration of an elementary teacher’s practice in a math classroom

ABSTRACT This study explores the responsiveness of an elementary teacher’s support for emergent bilingual students by examining the connections between the teacher’s assessment expertise, formative assessment practices, and emergent bilinguals’ unique language needs in math classes. The study, which investigated the indicators that characterize a linguistically responsive and equitable formative assessment, included and prioritized the perspectives of emergent bilingual students. Primary data sources included classroom observations, teacher interviews, and student interviews. This study elucidates pedagogical practices that are beneficial for emergent bilinguals. It found that both emergent bilinguals and teachers generally agree on the best practices, including their understanding of the language support. However, at times, there is a discrepancy between what the teacher said and the support that emergent bilingual students need. This study contributes to a comprehensive understanding of what and how teachers should know and act for performing responsive formative assessments for emergent bilinguals. It has implications for further research and for teachers who work with emergent bilingual students in core content areas.

Language exams as a policy instrument

Utilising language management theory (Jernudd and Neustupný 1987; Nekvapil 2016), this paper explores motivations for the language exam as an instrument of organised management in the form of policy in the Czech context and analyzes its outcomes more than ten years after its initial implementation. First, we provide an overview of research on language testing as a form of organised management, discussing how emphasis has been placed on citizenship, and explain the relevance of researching the use of language exams for permanent residence in the European Union, where an important distinction is made between EU and non-EU citizens. We consider the selection of the appropriate Common European Framework of Reference for Languages (CEFR) level for the exam and the overall question of exemptions, which may raise doubts about the exam’s overall effectivity as a tool for the linguistic integration of foreigners. We show that there are several tendencies in the understanding of the situation by individuals who have already passed the exam, including the view of it as a mere formality, and, conversely, as a major hurdle. We conclude by exploring potential policy consequences of these outcomes, emphasising the idea that language support from the state and employers needs to be provided intensively and immediately following the individuals’ arrival in the country.

BRAND: A platform for closed-loop experiments with deep network models.

Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g., Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g., C and C++). To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termed nodes, which communicate with each other in a graph via streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes. In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1-millisecond chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 milliseconds of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems. By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.&#xD.

"A different country, a different language, a different culture…" Educational experiences of international nursing students studying in Turkey: A qualitative study

AimThis study was aimed at investigating the educational experiences of international nursing students in Turkey. BackgroundNursing education has now gained an international dimension, which creates an environment where students from different cultures come together. In Turkey, nursing education is expanding internationally due to the increasing number of international students. DesignA descriptive- phenomenological qualitative study. MethodsThe study data were collected from nursing students who came to Turkey from 17 different countries between April 2023 and July 2023 to study at a nursing faculty in Turkey using a semi-structured interview form. Of these students, 27 constituted the study sample. The data collected were analyzed using the content analysis. The COREQ checklist was used in reporting the study. ResultsFour themes emerged: a) challenges, b) gains, c) support needs and d) recommendations. Among challenges reported by the participating students were language barriers, cultural adjustment issues and lack of social support. As for the gains of being a foreign student, they stated that they experienced intercultural interaction and improved their language skills, independence and self-confidence. In the study, the need for language support, provision of psychological assistance and cultural adaptation support for these students and the importance of practical recommendations such as mentoring programs and orientation trainings to improve their educational experience were emphasized. ConclusionIn conclusion, in this study, the importance of targeted strategies in nursing education in areas such as language support, psychological assistance and cultural adaptation was emphasized, which helps international students to have a successful educational experience. It was also concluded that mentoring programs and orientation trainings could enrich the educational processes of students by facilitating their adaptation to their new environment. This study is notable in the literature in that students from 17 diverse countries share the same program and the same prerequisites.

On the functioning of the Law «On Languages in the Republic of Sakha (Yakutia)» in the city of Yakutsk (based on the results of the questionnaire on Articles 19-23, 33)

In modern times, the functioning of languages in public space requires state regulation, the main goal of which should be the preservation and development of multilingualism and support of minority languages. The national politics in the field of preservation and development of the languages of the peoples of the Russian Federation is based on the implementation of a number of measures at the legislative level. Russia adopted the Law “On the Languages of the Peoples of the Russian Federation”; its national republics have their own laws on languages regulating the functioning of languages on their territory. In Yakutia, on October 16, 1992, Law of the Republic of Sakha (Yakutia) No. 1170-XII “On languages in the Republic of Sakha (Yakutia)” was adopted, a study on the implementation of which became one of the projects of the state program of the Republic of Sakha (Yakutia) “Preservation and development of state and official languages in the Republic of Sakha (Yakutia) for 2023.” The project “Monitoring the implementation and compliance with the requirements of articles of the Law on Languages in Yakutsk of the Republic of Sakha (Yakutia)” was proposed by the Department of Stylistics of the Yakut Language and Russian-Yakut Translation of the Institute of Languages and Culture of the North-East of the Russian Federation of the M.K. Ammosov North-Eastern Federal University. In order to implement the project, the department’s team developed a questionnaire on the articles of the Law “On Languages in the Republic of Sakha (Yakutia)” for two target audiences: specialists from organizations to whose work the articles of the Law are directly related, and the population. The purpose of the proposed work is to analyze the results of a survey conducted on articles 19, 20, 21, 22, 23, 33 among specialists of organizations and the population of the city of Yakutsk. The study used statistical and descriptive methods, as well as data processing and analysis methods. The results of the study are structured as follows: first, the full text of the considered article of the Law “On Languages in the Republic of Sakha (Yakutia)” is presented, then an analysis of a survey among employees of the institution and the results of a survey among the population are presented, followed by recommendations for the implementation of the article of the Law.

UA-LLM: ADVANCING CONTEXT-BASED QUESTION ANSWERING IN UKRAINIAN THROUGH LARGE LANGUAGE MODELS

Context. Context-based question answering, a fundamental task in natural language processing, demands a deep understanding of the language’s nuances. While being a sophisticated task, it’s an essential part of modern search systems, intelligent assistants, chatbots, and the whole Conversational AI field. While English, Chinese, and other widely spoken languages have gathered an extensive number of datasets, algorithms, and benchmarks, the Ukrainian language, with its rich linguistic heritage and intricate syntax, has remained among low-resource languages in the NLP community, making the Question Answering problem even harder.&#x0D; Objective. The purpose of this work is to establish and benchmark a set of techniques, leveraging Large Language Models, combined in a single framework for solving the low-resource problem for Context-based question-answering task in Ukrainian.&#x0D; Method. A simple yet flexible framework for leveraging Large Language Models, developed as a part of this research work, enlights two key methods proposed and evaluated in this paper for dealing with a small amount of training data for context-based question-answering tasks. The first one utilizes Zero-shot and Few-shot learning – the two major subfields of N-shot learning, where N corresponds to the number of training samples, to build a bilingual instruction-based prompt strategy for language models inferencing in an extractive manner (find an answer span in context) instead of their natural generative behavior (summarize the context according to question). The second proposed method is based on the first one, but instead of just answering the question, the language model annotates the input context through the generation of question-answer pairs for the given paragraph. This synthetic data is used for extractive model training. This paper explores both augmentation-based training, when there is some annotated data already, and completely synthetic training, when no data is available. The key benefit of these two methods is the ability to obtain comparable prediction quality even without an expensive and long-term human annotation process.&#x0D; Results. Two proposed methods for solving the low-to-zero amount of training data problem for context-based questionanswering tasks in Ukrainian were implemented and combined into the flexible LLM experimentation framework.&#x0D; Conclusions. This research comprehensively studied OpenAI GPT-3.5, OpenAI GPT-4, Cohere Command, and Meta LLaMa-2 language understanding capabilities applied to context-based question answering in low-resource Ukrainian. The thorough evaluation of proposed methods on a diverse set of metrics proves their efficiency, unveiling the possibility of building components of search engines, chatbot applications, and standalone general-domain CBQA systems with Ukrainian language support while having almost zero annotated data. The prospect for further research is to extend the scope from the CBQA task evaluated in this paper to all major NLU tasks with the final goal of establishing a complete benchmark for LLMs’ capabilities evaluation in the Ukrainian language.

Development of a multilingual online course with the language support for teaching physics in English

&lt;span lang="EN-US"&gt;Distance education offers an alternative to traditional teaching methods, particularly online courses. During the pandemic, the role of information and communication technologies in the field of education has increased and is used to organize both synchronous and asynchronous learning. However, not all information and communication technology (ICT) tools and platforms provide the necessary functionality for developing online courses in the direction of language integration. This article discusses the issues of creating a multilingual online course of physics in English with the necessary language support and additional functions for the development of students’ academic language. The study used such methods as a survey, focus groups, analytical and descriptive methods, and empirical research. The key features of the developed course are the use of various types of content and methods for assessing both subject and language knowledge and skills. The presented survey results, data on summative assessment (SE), and trial testing show the effectiveness of the developed course modules. The conclusions and results described in the article will be useful to teachers of natural sciences in general education and private schools, especially those using international programs, deputy directors, as well as teachers who teach their subjects in a second or third language.&lt;/span&gt;

Health Literacy in Pediatric Consultations on Allergy Prevention.

The first 3 years of life offer an opportunity to prevent allergic diseases. Pediatricians are an important source of health information for parents. However, a certain degree of health literacy is necessary to understand, appraise, and apply preventive behavior, which can be supported by health literacy (HL) sensitive consultations and a HL friendly environment. In this study, we want to shed light on how pediatricians in outpatient care in Germany advise on early childhood allergy prevention (ECAP) and how they consider parental HL. We conducted 19 semi-standardized telephone interviews with pediatricians from North-Rhine-Westphalia and Bavaria. The interviews were audio-recorded, transcribed, pseudonymized, and subjected to content analysis. Current ECAP recommendations were well known among our sample. Despite the shift of evidence from avoidance of allergens toward early exposure, providing advice on ECAP was considered non-controversial and it was widely assumed that recommendations were easy to understand and apply for parents. However, ECAP was treated as an implicit topic resonating among others like infant nutrition and hygiene. Regarding HL, our interview partners were not aware of HL as a concept. However, they deemed it necessary to somehow assess parental information level and ability to understand provided information. Formal HL screening was not applied, but implicit strategies based on intuition and experience. Concerning effective HL-sensitive communication techniques, interviewees named the adaptation of language and visual support of explanations. More advanced techniques like Teach Back were considered too time-consuming. Medical assistants were considered important in providing an HL-sensitive environment. Time constraints and the high amount of information were considered major barriers regarding HL-sensitive ECAP counseling. It seems warranted to enhance professional education and training for pediatricians in HL and HL-sensitive communication, to reach all parents with HL-sensitive ECAP counseling. [HLRP: Health Literacy Research and Practice. 2024;8(2):e47-e61.].

Impact of Student Engagement in Language Support Classes Through Cooperative Learning: A Study of Pakistani Educational Institutions

Cooperative learning represents a pedagogical approach where students of varying abilities collaborate in small groups to achieve common goals. It involves the implementation of diverse learning activities to deepen understanding of the subject matter. Within these groups, learners engage in discussions, exchange ideas and knowledge, seek additional information, and present their findings to the class. Notably, cooperative learning prioritizes student-centeredness over teacher-centeredness, placing greater emphasis on learning objectives rather than performance targets. This shift also diminishes the focus on competitive assessments, urging educators to embrace alternative evaluation methods. Pressel (1992) asserts that cooperative learning enhances learning outcomes and retention, bolsters confidence and intrinsic motivation, and cultivates a more positive attitude toward academic and social abilities.

Addressing Pedagogical Challenges Faced by English Lecturers at Halabja University, Kurdistan: A Qualitative Study

This study addresses the challenges faced by English lecturers at the College of Basic Education at Halabja University, Kurdistan, aiming to contribute insights that can inform improved regulations and enhance English Language Teaching (ELT) at the university. The data collected via semi-structured interviews was subjected to thematic analysis, and five English lecturers participated. The results of this study focus on the obstacles encountered by English instructors, exploring themes such as student motivation, language competence, class size, time constraints, and inadequate facilities. The study underscores the need for targeted interventions to address these challenges and improve the overall quality of ELT. Based on the findings, specific recommendations include implementing motivational strategies in the classroom, providing additional language support resources for students, reducing class sizes where possible, allowing more preparation time for lecturers, and upgrading classroom facilities and teaching materials. The findings highlight English lecturers' intricate issues, emphasize the necessity for institutional and governmental support to facilitate a more conducive teaching and learning environment, and reveal significant challenges, including students' lack of motivation, linguistic proficiency issues, difficulties managing large classes, time constraints, and insufficient teaching resources

The Institutional and Social Support Survey (ISS‐10): Quantifying international faculty language support

AbstractThe purpose of this study was to develop and test the internal and external reliability of a novel research instrument which measures language support for international faculty members and its effects on integration. While previous research has focused on the contributions of international faculty and efforts to attract them, growing concerns about negative experiences and low rates of retention have led scholars to focus on institutional linguistic support. Specifically in emerging host countries, interest exists for the role of support for learning the local language. In this context, this study substantiates an instrument, the Institutional and Social Support Survey (ISS‐10) which quantifies social support and language support provided by host universities. The instrument was first refined using Rasch rating scale analysis and exploratory factor analysis (EFA). Subsequent linear mixed effects (LME) regression on a second administration of the instrument revealed that the ISS‐10 significantly predicts the integration of international faculty as quantified by the IPL‐12, an often‐used measure of migrant integration. The ISS‐10 may, therefore, be of interest to scholars researching international faculty integration where local language proficiency is a concern.

Exploring the interplay of semantic knowledge in bilingual children: heritage language and second language

This study investigates the semantic development of heritage bilingual preschool children aged 3 to 5 who acquire Cantonese as their heritage language (HL) at home and English as their community language (L2) in school settings. The research examines how bilingual children organize and access their vocabulary in two distinct languages and how their heritage language influences semantic development in L2. We examined their performance in Word Association Identification Task (WAID) and Word Association Task (WAT) in both languages. Results showed that they perform similarly in WAID in both languages, with higher accuracy in semantically unrelated conditions. The WAT results showed that children had more syntagmatic responses in Cantonese than in English, but had similar paradigmatic responses in both languages. Regression analysis revealed that paradigmatic responses in Cantonese predicted children's English paradigmatic responses. Their English paradigmatic responses were also associated with WAID performance in English. This study contributes to understanding heritage bilingual children's semantic development, with implications for education and language support.

How interpreting influences defendants’ participation: a discursive study of zero renditions and non-renditions in court interpreting

Abstract The inquisitorial feature of Chinese criminal trials requires active participation from defendants in courtroom interaction. To fully participate in the trial proceedings, defendants who cannot speak the language of the court have to rely on the language support offered by interpreters. Court interpreters are expected to facilitate language minorities’ participation and communication in the proceedings, but their mediation changes the dynamics of courtroom interaction. This paper aims to explore how the use of interpreting impacts the participation status of English-speaking defendants in Chinese criminal trials. Adopting participation framework as the primary analytical tool, drawing upon discourse analysis of seven authentic recordings of bilingual trials and fieldwork observations, this study empirically shows that as a result of zero renditions and non-renditions in the triadic communication, the participation role of the defendant changes from being a direct addressee to the position of a non-participant, which results in them a marginalised status. The role alteration, which is associated with the court interpreting practice, including the sole use of consecutive interpreting, puts the defendant at a disadvantage, undermining their participation rights in the trial proceedings. In a way, the defendant becomes a de facto stand-by participant in the interpreter-mediated courtroom interaction.

Acculturative Stress of Asian International Students before and during the COVID-19 Pandemic

Acculturative stress may hamper international students' ability to succeed in the United States of America. This study analyzed the impact of a range of factors noted in the literature and compared the levels of stress they were related to before and during the COVID-19 pandemic. We collected three waves of data (total responses = 204) via an online survey from ten universities/colleges across different regions in the United States of America. The results indicated that English proficiency and sex predicted the overall acculturative stress of Asian international students, in which higher perceived English proficiency predicted a lower perceived acculturative stress, and female Asian international students experienced higher acculturative stress than males. Also, Asian international students are experiencing more sadness during the pandemic and their peers are more discriminated against during 2020. Thus, university administrators should promote English language programs and support services (e.g., gender specific student support) to effectively address Asian students’ concerns.

Language dynamics and agency in multilingual research interviews

AbstractIn a world of increasing linguistic diversity, questions of language use and language ideologies in research interviews are gaining increasing importance. This article reports and reflects on language use in research interviews with former refugees in New Zealand and Sweden. In the interviews, multilingual speakers had the option to engage in the host language (English or Swedish, respectively), to bring their own language support person, or to request a professional interpreter. The article suggests that providing these options enabled participants to engage a greater range of linguistic and multimodal resources to create meaning and construct their identities, and also provided opportunities for language learning in the interviews. The article also highlights the importance of acknowledging the role of interpreters in the co‐construction of meaning and what this means for data collection and findings.

Enhancing Natural Language Processing (NLP) through VIKOR Method: A Comprehensive Approach for Improved Computational Linguistics

Natural Language Processing (NLP) is a revolutionary discipline that resides at the crossroads of computer science and linguistics, with its primary emphasis on the interaction between computers and human language. With roots in artificial intelligence, NLP seeks to equip machines with the ability to comprehend, interpret, and respond to natural language, enabling more intuitive and meaningful communication between humans and computers. This multidisciplinary domain leverages advanced algorithms and models to tackle a range of linguistic challenges, from language translation and sentiment analysis to speech recognition. Recent breakthroughs, particularly in deep learning and neural networks, have propelled NLP to new heights, with applications spanning diverse sectors such as healthcare, finance, and education. As NLP continues to advance, its potential impact on enhancing human-machine interaction and information processing is increasingly evident, promising a future where technology seamlessly integrates with our natural modes of communication. The significance of Natural Language Processing (NLP) in research lies in its capacity to revolutionize human-computer interaction. NLP empowers machines to understand and generate human language, enabling advancements in sentiment analysis, language translation, and conversational AI. This transformative capability has profound implications across various industries, including healthcare, finance, and education. NLP research not only enhances the efficiency of information retrieval and processing but also holds the key to developing more intuitive and user-friendly technologies. As the frontier of NLP research expands, its potential to bridge the communication gap between humans and machines continues to shape the future of technology and information access. VIKOR functions as a multi-criteria decision-making technique that determines the best option by comparing it to the ideal option. The ranking procedure comprises determining distances concerning the optimal solution. VIKOR uses linear normalization to get the best possible outcomes. This technique was first presented by Opricovic in 1998 and was intended to optimize multi-attribute complex systems. Its main focus is on ranking lists that allow for flexibility in the strategy weight interval and incorporate compromise alternatives to obtain desired results. Alternative taken as Open AI GPT-4, Google BERT, Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding, spaCy, NLTK (Natural Language Toolkit), Amazon Comprehend. Evaluation Parameter taken as Sentiment Analysis (F1 Score), NER Performance, Language Support, Processing Speed (Response Time) (ms), Translation Accuracy (BLEU Score), Data Privacy. The result it is seen that Amazon Comprehend is got the first rank where as is the IBM Watson Natural Language Understanding is having the lowest rank.