Analysis Of Resources Research Articles

How Innovation Plays a Role in Banking and Increases Profitability

Banking as an extensive information subject is constantly changing under the marketing influences of the era of huge information. Checking out the revolutionary important specifics analytic sources, as Data Mining techniques are essential for the banking business. This aims to reveal valuable information from the vast length of info and realize far better strategic management in addition to client pleasure. To provide decent assistance for future research and development, a most thorough present analysis of the existing study problem of DM in banking will be unbelievably beneficial. Since pre existing ratings just tackle the applications until 2013, this specific newspaper seeks to run this specific exploration gap, and also supplies the sizable progressions and most current DM implementations in banking document 2013. By collecting and analyzing the fads of review concentration, info online resources, specialized aids, and info analytical online resources, this specific newspaper helps obtain important insights about the succeeding developments of equally DM together with the banking business, in addition to a comprehensive one stop guide table. Furthermore, we recognize the main obstacles and also provide a summary for all those interested in big data.

A Bibliometric Analysis of Current Knowledge Structure and Research Progress Related to Urban Community Garden Systems

Community gardens offer broad research opportunities and analytical resources encompassing urban planning to environmental sustainability, food systems, and social capital. However, little is known about the knowledge structure and research development related to community gardens. This study presents an in-depth bibliometric performance analysis and visual scientific mapping analysis of the literature on community gardens by examining 487 published papers selected from the Web of Science database. The results indicated a considerable rise in research papers in this subject area from 2012 onwards, with most contributions from the United States. Studies from high-income countries accounted for 93.22%, and 38 countries have cooperated 167 times in this field. “Health” is the most frequent keyword, and the terms “ecosystem services” has been gaining popularity over the last five years. A combination of co-citation clustering and keyword co-occurrence clustering analysis identified three major research themes in the field of community gardens: “ecosystem services and disservices”, “multidimensional association”, and “sustainable garden systems”. The development of ecosystem value assessment frameworks, the establishment of region-wide soil monitoring databases, accounting for the cost-effectiveness of nature-based solutions, the integration of garden systems into smart cities, and the integration of water management into regulation will be important future research directions regarding community gardens. Overall, this study provides scholars with a systematic and quantitative understanding of community gardens.

Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration

To realize value from their wealth of digital data, organizations are investing in data-driven organizational initiatives—efforts in which they must draw expertise in data, algorithms, and visualization together with knowledge and skills in business domains such as marketing and human resources. However, they face the challenge of crossing the knowledge divide between analytics groups and business groups. Exploring relationships between the two groups in 37 data-driven organizational initiatives, we develop a configuration-based model that explains analytics and businessdomain knowledge integration through the lens of synergy. Our configurational analyses revealed five configurations of relationships between the two, which bring about two distinct change outcomes: “dedicated data groups” and “multidisciplinary teams” lead to the emergence of new datadriven ways to work, and “analytics institutionalization,” “analytics resource optimization,” and “networked communities” produce convergence, through the sharing of data-driven ways to work. Each configuration displays a distinct element of the core processes identified (“developing group connectedness,” “exchanging analytics and business domain knowledge,” and “incentivizing organizational data use”) and yields either an emergence or convergence of data-driven ways of working. The findings demonstrate how data-driven organizational initiatives can benefit from a pervasive form of organizing that entwines analytics groups and business groups such that their members’ tools, mindsets, and behaviors are merged to profoundly change ways of working. Together, these findings and the configurational methodology used provide a nuanced picture of how organizations integrate the requisite specialist knowledge across domains to realize value from data.

The Influence of Price Factors on the Strategic Development of the Agricultural Sector in Various Natural and Economic Conditions

The purpose of the study is to substantiate the directions for improving state support for the strategic development of the agricultural sector. The information and analytical resources of the study were the spatial database compiled by the authors in the context of the subjects of the Russian Federation by indicators on average for 2017-2019. The influence of price factors on the formation of physical and economic accessibility of products has been studied. It was revealed that in the process of formation the price interests of sellers and buyers have different polarity. In order to create a balanced formation of physical and economic accessibility of products, it is necessary to increase prices to create conditions for expanded reproduction of producers’ resources, on the one hand, and reduce prices to increase the purchasing power of the population in food products, on the other hand. Moreover, the influence of natural and economic factors simultaneously on the conditions of reproduction and purchasing power is observed. It is proposed to supplement the provisions on the classification of state support for the agricultural sector with measures to subsidize the sphere of production and the sphere of consumption of products, the level of which is advisable to differentiate on a territorial basis.

THE ROLE OF GLOBAL SCHOLARSHIP IN THE JOURNAL OF AGING & SOCIAL POLICY

Abstract Policymakers, practitioners, and researchers need a balanced, thoughtful, and analytical resource to meet the challenge of global aging at a rate that’s historically unprecedented. The Journal of Aging & Social Policy (JASP), which was founded in 1989, serves this role by drawing contributions from an international panel of policy analysts and scholars who assume an interdisciplinary perspective in examining and analyzing critical phenomena that affect aging and the development and implementation of programs for older adults from a global perspective. Study settings extend beyond the United States to include Europe, the Middle East, Australia, Latin America, Asia, and the Asia-Pacific rim. This presentation will document the scope, content, and focus of JASP, including the rise of international submissions, which now account for approximately half of articles published. Opportunities for publishing in JASP will be discussed; so too will strategies for navigating the peer-review process successfully.

The Memphis Pandemic Health Informatics System (MEMPHI-SYS)-Creating a Metropolitan COVID-19 Data Registry Linked Directly to Community Testing to Enhance Population Health Surveillance.

The current coronavirus disease (COVID-19) pandemic has placed unprecedented strain on underfunded public health resources in the Southeastern United States. The Memphis, TN, metropolitan region has lacked infrastructure for health data exchange.This manuscript describes a multidisciplinary initiative to create a community-focused COVID-19 data registry, the Memphis Pandemic Health Informatics System (MEMPHI-SYS). MEMPHI-SYS leverages test result data updated directly from community-based testing sites, as well as a full complement of public health data sets and knowledge-based informatics. It has been guided by relationships with community stakeholders and is managed alongside the largest publicly funded community-based COVID-19 testing response in the Mid-South. MEMPHI-SYS has supported interactive Web-based analytic resources and informs federally funded COVID-19 outreach directed toward neighborhoods most in need of pandemic support.MEMPHI-SYS provides an instructive case study of how to collaboratively establish the technical scaffolding and human relationships necessary for data-driven, health equity-focused pandemic surveillance, and policy interventions.

Leveraging H3Africa Scholarly Publications for Technology-Enhanced Personalized Bioinformatics Education

The Coronavirus Disease 2019 (COVID-19) pandemic has catalyzed the expectations for technology-enhanced interactions with personalized educational materials. Adjusting the content of educational materials to the geographical location of a learner is a customization feature of personalized education and is used to develop the interest of a learner in the content. The educational content of interest in this report is bioinformatics, in which the knowledge spans biological science and applied mathematics disciplines. The Human Heredity and Health in Africa (H3Africa) Initiative is a resource suitable for use when obtaining data and peer-reviewed scholarly articles, which are geographically relevant and focus on authentic problem solving in the human health domain. We developed a computerized platform of interactive visual representations of curated bioinformatics datasets from H3Africa projects, which also supports customization, individualization and adaptation features of personalized education. We obtained evidence for the positive effect size and acceptable usability of a visual analytics resource designed for the retrieval-based learning of facts on functional impacts of genomic sequence variants. We conclude that technology-enhanced personalized bioinformatics educational interventions have implications in (1) the meaningful learning of bioinformatics; (2) stimulating additional student interest in bioinformatics; and (2) improving the accessibility of bioinformatics education to non-bioinformaticians.

Adaptive water governance research in social sciences journals: a bibliometric analysis

Abstract Adaptive water governance plays an increasingly important role in sustainable urban development and water governance response to global climate change. To comprehensively understand the research situation and development trend of adaptive water management, this study conducts a systematic literature review of articles published in International Social Sciences Citation Index (SSCI) journals based on bibliometric analysis. The results show that adaptive water governance as an emerging topic of water governance has an overall growth trend in published articles. The articles on adaptive water governance mainly concentrated on the disciplines of environmental science and environmental studies. Developed countries are a hub for water governance research, and China has the sixth largest number of articles from around the world. The adaptive water governance research has formed a preliminary global collaborative network, but the authors' collaboration needs to be strengthened. The most popular topics of adaptive water governance include South Africa, adaptive management, groundwater, principal component analysis, scenario planning, the analytic hierarchy process, resources, basins, computer experiments, and technology development. This finding suggests that adaptive water governance is a critical driver for sustainable urban development and represents a critical direction in the future research of water governance.

Developing a learning analytics resource with meaningful data for first year teachers

It is known that due to the large, diverse sets of data captured in learning analytics, clear display of information is crucial to its success. Here, we describe a concise learning analytics resource, the Cohort Snapshot, that has been developed using a human centred approach, with input from experienced academics from across the institution. Meaningful data is presented as a unit level summary and includes program enrolment, student demographics, grade distributions and LMS activity. The data that was used to develop the resource was accessed via the university’s data warehouse which was synthesised using the R programming language. The Cohort Snapshot provides teaching academics, including sessional staff, access to data, to allow the adaption of teaching pedagogy to meet the needs of increasingly diverse student cohorts in a regional Australian university.

Systematic risk assessment in an investment project: The problem of definition

Aim. The presented study aims to analyze existing approaches and develop recommendations for the numerical assessment of the magnitude of risk associated with the implementation of investment projects.Tasks. The authors describe traditional approaches to systematic risk assessment; systematize the practice of determining the beta coefficient as a risk measure of investment projects; analyze alternative approaches to assessing discount rate and systematic risk; develop recommendations for leveling the systematic risk measure.Methods. This study uses general scientific methods of analysis and synthesis, induction and deduction, comparison and description, as well as special methods of financial mathematics and economic-mathematical modeling.Results. Determining the investment attractiveness of a business or project involves forecasting and discounting future cash flows. Obvious risks are accounted for by adjusting cash flows, and most other risks and uncertainties are reflected in the discount rate. One of the specific components of the discount rate is a measure of systematic risk – the beta coefficient. The authors consider the traditional approach to assessing the cost of capital and systematic risk, alternative approaches that have methodological advantages, but are often not applicable in practice due to additional difficulties in calculations, and most importantly – in comparison between projects/ companies/results for different settlement dates. The study also provides a critical analysis of publicly available analytical data through the example of A. Damodaran’s information and analytical resource and highlights problems in the use of statistics for the valuation of projects and companies in the long term due to the significant volatility of the beta coefficient.Conclusions. In the absence of other reliable sources, analysts should be more careful about the values of published analytical materials, in some cases independently rechecking the data of publicly available analytics using the recommendations presented in this study.

Patients Seen in Radiation Oncology Who Use Cannabis Are More Likely to Also Have Active Prescriptions for Opiates or Benzodiazepines

<h3>Purpose/Objective(s)</h3> With changes in state legal frameworks governing cannabis access, more people in the United States have begun using cannabis. Individuals with cancer endorse many reasons for cannabis use in the face of limited data. Some have suggested that cannabis may serve as an alternative to prescription medications such as opioids, benzodiazepines, and sleep aids for symptom management. We hypothesized that patients with cancer using cannabis would be less likely to have active prescriptions for these medication classes commonly used to manage pain, anxiety, and insomnia, respectively. <h3>Materials/Methods</h3> Clinician-collected cannabis history data documented via an electronic medical record-embedded form were abstracted using Michigan Radiation Oncology Analytics Resource (M-ROAR) for adult patients seen in an academic radiation oncology department from 10/2020 to 11/2021 under an IRB-approved protocol. Logistic regression was used to explore associations between recent cannabis use and prescriptions for narcotics, benzodiazepines, and sleep aids. <h3>Results</h3> Data were available for 3059 unique patients, including 343 (11.2%), 235 (7.7%), and 2481 (81.1%) endorsing recent (in the last month), non-recent only (use of cannabis in the past but not in the last month) and no history of cannabis use, respectively. Among those who recently used cannabis, 6.1% used cannabis more than 1 time per day, 54% used cannabis daily, and 21% used cannabis at least weekly but not every day. Most patients smoked cannabis (46%) or used edible forms of cannabis (35%). The most common reasons for cannabis use were pain (27%), insomnia (18%), and anxiety (15%), so we examined concurrent use of cannabis along with prescription opiates, sleep aids, and benzodiazepines. Patients who recently used cannabis were more likely to have an active prescription for an opioid medication (OR: 1.52; 95% CI: 1.21, 1.905; p <0.001) than patients who did not report recent cannabis use. There was no relationship between sleep aid medication prescription and recent cannabis use (OR: 0.62; 95% CI: 0.27, 1.43; p=0.261), but individuals using cannabis were more likely to have an active prescription for a benzodiazepine (OR: 1.40; 95% CI: 1.07, 1.82; p = 0.013). <h3>Conclusion</h3> The use of prescription opiates and prescription benzodiazepines is more prominent in patients who endorse cannabis use. Our data therefore do not support the hypothesis that cannabis use eliminates the need for medications to manage pain or anxiety. This suggests that for many patients, cannabis is used as an adjunct alongside conventional evidence-based therapies for symptom control and not as an alternative to such agents. Patient education is key to help individuals with symptoms locate evidence-based therapies and optimize them for safety and efficacy as we await high quality evidence that supports or refutes the efficacy of cannabis for management of many symptom classes in patients with cancer.

High-throughput protein binding assay using cross-sample pooling in combination with high-resolution mass spectrometry.

The fraction of unbound drugs (ƒu ) is a useful pharmacokinetic parameter in understanding drug disposition (Absorption, Distribution, Metabolism, Excretion), pharmacological activity and toxicity. Therefore, protein binding assays are frequently performed in drug development, creating a high demand for biological, experimental and analytical resources. Our work aims to increase binding assay throughput and comprehensiveness, while reducing biological and experimental consumption without compromising data quality by introducing cross-pooling and cassetting procedures, followed by a rapid and informative high-resolution mass spectrometry (HRMS) analysis. Individual drugs were spiked into a test matrix and incubated in a rapid equilibrium dialysis device. After incubation, a cross-pooling procedure was performed, in which the samples of one drug were equalized with the complementary matrix provided from a different drug. The same drugs were also assayed with a conventional method, in which samples were equalized with the newly prepared complementary matrix. Cross-pooled samples were further cassetted to increase throughput. The samples were analyzed by high-performance liquid chromatography coupled with an Orbitrap HRMS, and the fu values were calculated and compared between the cross-pooling and conventional sampling procedures. Highly comparable human plasma fu values of 27 drugs representing different chemical classes and wide-ranging fu values were obtained by conventional and cross-pooling procedures, The tight correlation was further validated in other species (rat, mouse) and matrices (microsomes, brain). In addition, the cassetted samples showed highly consistent fu values compared to their noncassetted counterparts. Moreover, HRMS analysis not only showed highly consistent and repeatable quantification results compared to the "gold standard" triple quadrupole (QqQ)analysis, but also demonstrated outstanding advantage over QqQ in enabling a high-throughput, informative and versatile analysis. This work demonstrates that the cross-pooling procedure with further sample cassetting using HRMS is experimentally and analytically feasible to allow a higher throughput (increased by up to 8-fold), resource-effective (reducing matrix consumption by 50%, minimizing time spent on method development and platemap design), analytically dependable (accurate quantification), and versatile (metabolite elucidation and low recovery troubleshooting) analysis.

A whole-brain 3D myeloarchitectonic atlas: Mapping the Vogt-Vogt legacy to the cortical surface

Building precise and detailed parcellations of anatomically and functionally distinct brain areas has been a major focus in Neuroscience. Pioneer anatomists parcellated the cortical manifold based on extensive histological studies of post-mortem brain, harnessing local variations in cortical cyto- and myeloarchitecture to define areal boundaries. Compared to the cytoarchitectonic field, where multiple neuroimaging studies have recently translated this old legacy data into useful analytical resources, myeloarchitectonics, which parcellate the cortex based on the organization of myelinated fibers, has received less attention. Here, we present the neocortical surface-based myeloarchitectonic atlas based on the histology-derived maps of the Vogt-Vogt school and its 2D translation by Nieuwenhuys. In addition to a myeloarchitectonic parcellation, our package includes intracortical laminar profiles of myelin content based on Vogt-Vogt-Hopf original publications. Histology-derived myelin density mapped on our atlas demonstrated a close overlap with in vivo quantitative MRI markers for myelin and relates to cytoarchitectural features. Complementing the existing battery of approaches for digital cartography, the whole-brain myeloarchitectonic atlas offers an opportunity to validate imaging surrogate markers of myelin in both health and disease.

Analysis of AI based Data Wrangling Methods in Intelligent Knowledge Lakes

A novel conception of Knowledge Lake, i.e., a Contextualized Data Lake is to be soundly educated. The deliberated big-data practices pave a means for the erection of Intelligent Knowledge Lakes and that being the resources for big-data applications and analytics. This analysis likewise opens the welfares, disputes, and exploration prospects of Intelligent Knowledge Lakes. Data Science is launched as an influential discernment through businesses. Organizations today are dedicated on transforming their facts into ultra-practical intuitions. This work is challenging, as in present day’s intelligence, amenity and cloud customary budget trades accumulate immense aggregates of unprocessed data after a variety of funds. Data Lakes are familiar as a packing depository that fetch concurrently the unprocessed data in its innate set-up (sustaining to NoSQL from relational databases) which is crucial. The logic behind Data Lake is to stockpile unprocessed data and let the data analyst resolve the way to curate them well ahead of reviewing the idea of Knowledge Lake, which is an anecdotal Data Lake. The Intelligent Knowledge Lake stipulate the basis for big data analytics by robotically curating the unprocessed data in the Data Lake grooming these for stemming intuitions via programmed interactive real-time optimized data wrangling in intelligent knowledge lakes. Computerization of an exposed free public Data and Knowledge Lake amenity provides developers and researchers a distinct REST API to systematize, curate, catalog and interrogate their data and metadata in the Lake for a longer time. It administers manifold database/databank know-hows (from Relational to NoSQL) that deals with an inherent scheme for data security, curation, and provenance.

Spatio-Temporal Differences in Economic Security of the Prefecture-Level Cities in Qinghai-Tibet Plateau Region of China: Based on a Triple-Dimension Analytical Framework of Economic Geography.

The assessment of regional economic security (RES) is mainly based on the theoretical ideas of political economy and marginalism, and the research areas are mainly concentrated in European and American countries/regions, especially Eastern Europe. Taking the Qinghai–Tibet Plateau in China as an example, this paper constructs a triple-dimensional analytical framework, resources, and environmental–economic foundation-driving forces, based on the institutional approach of economic geography, with the purpose of making up for the deficiency of the extant literature, which pays little attention to regional characteristics and the dynamic mechanism concerning RES, and to provide a tool to identify key factors affecting RES. This paper obtained the main conclusions as follows. (1) The index of the economic security in the Qinghai–Tibet Plateau is on the rise, and the difference at the level of RES among cities is significant but tends to decrease. (2) There is a significant spatial autocorrelation among cities in the Qinghai–Tibet Plateau in terms of RES. The high-value areas are concentrated along the southeast edge, and the low-value areas are concentrated in the central areas of the west. (3) Despite lower weight values, the weakness of the economic foundation and the fragility of the ecological environment has increasingly hampered the improvement of the economic security in the Qinghai–Tibet Plateau. In terms of driving forces, it is the support of the central government and aid programs of other provinces that contributes to its economic development.

People Analytics and Human Resource Management: How the Use of Smart Data Can Improve the Training Processes

People Analytics (PA), which applies big data in human resources management (HRM), is becoming increasingly important in the current globalised and digitalised era. Despite several issues due to PA, such as high costs and privacy legislation, PA improves employees’ well-being and helps organisations create value on the market to become more competitive, achieving sustainable development, as defined in the 17 Sustainable Development Goals proposed by the United Nations. Based on the existing literature, the paper analyses the positive and negative aspects of the use of data for the design, monitoring and evaluation of the training process, especially in consideration of the recent technological changes and the impact on organisations’ processes triggered by the Sars-Cov-2 pandemic. Using data-based analysis, organisations can create a personalised training program for each employee to achieve the expected results. Therefore, to assess whether the training has been effective and whether the investment has been profitable, organisations should use specific and innovative tools and competencies. In conclusion, this investigation seeks to demonstrate that using PA in the training process is becoming increasingly relevant in organisations and, despite risks and costs, the benefits are considerable.

Information and analytical resources on the development of modern parliamentarism

The interaction of national parliaments in the international arena requires information support, which is provided not only by international parliamentary organizations and associations, but also by creating special web platforms that contain a variety of information about the activities, problems and prospects of parliaments and determine the general trends in the development of the institution of parliamentarism. The article describes the AGORA web portal, which positions itself as a "leading global platform for knowledge about parliamentary development." Its aim is to centralize and share knowledge on the development of parliamentarism, as well as to develop and demonstrate high-quality resources to support parliaments and provide relevant information to politicians and academic circles. In November 2020, a new modernized version of AGORA was launched, which includes three areas: 1) accumulation and exchange of knowledge in the field of parliamentarism, 2) coordination of exchange of experience and development of recommendations on best practices in parliamentary activities, 3) development and implementation of e-learning courses for parliamentarians and parliamentary staff on various issues (climate, gender, budgets, interaction with civil society). The most interesting and innovative materials of AGORA are analyzed, special emphasis is placed on the problems of development of modern Ukrainian parliamentarism and specific activities of the Verkhovna Rada of Ukraine. The facts presented in the article show that the international web portal AGORA is an authoritative factor in shaping the views of the international community on the processes and events of modern parliamentary development. The use of his publications will contribute to a clearer understanding and adequate assessment of the state of affairs in the field of representative democracy and the exercise of democracy.

An integrated atlas of human placental development delineates essential regulators of trophoblast stem cells.

ABSTRACTThe trophoblast lineage safeguards fetal development by mediating embryo implantation, immune tolerance, nutritional supply and gas exchange. Human trophoblast stem cells (hTSCs) provide a platform to study lineage specification of placental tissues; however, the regulatory network controlling self-renewal remains elusive. Here, we present a single-cell atlas of human trophoblast development from zygote to mid-gestation together with single-cell profiling of hTSCs. We determine the transcriptional networks of trophoblast lineages in vivo and leverage probabilistic modelling to identify a role for MAPK signalling in trophoblast differentiation. Placenta- and blastoid-derived hTSCs consistently map between late trophectoderm and early cytotrophoblast, in contrast to blastoid-trophoblast, which correspond to trophectoderm. We functionally assess the requirement of the predicted cytotrophoblast network in an siRNA-screen and reveal 15 essential regulators for hTSC self-renewal, including MAZ, NFE2L3, TFAP2C, NR2F2 and CTNNB1. Our human trophoblast atlas provides a powerful analytical resource to delineate trophoblast cell fate acquisition, to elucidate transcription factors required for hTSC self-renewal and to gauge the developmental stage of in vitro cultured cells.

Ukrainian industry in the world "landscape": trends in the context of long-term development objectives

The article presents results of a scientific and analytical study of the main trends in the Ukrainian industry, which characterize the features of its functioning and development in the last two decades against the background of similar processes in the world. The main accents are made on the analysis of the compliance of tendencies in Ukraine with the world and European trends of dynamic and structural development of industry. The processes of implementation in the national industry of the SDG-9 sectoral goal as a set of tasks within the framework of the concept of sustainable and inclusive development of manufacturing have also been studied. For comparative analytical studies, information and analytical resources of recognized international institutions were used, which systematically carry out constant monitoring and rating assessments of the current state and development of processes in the world economy on general methodological principles. It has been established that currently the industry of Ukraine is dominated mainly by short-term trends, which are formed and quickly change under the influence of market fluctuations. Starting from 2013, the leading role in accelerating the negative phenomena is played by the foreign policy factor – the military aggression of the Russian Federation. A factor that significantly worsens the current state of competitiveness and the potential for long-term industrial development of Ukraine is the decline of high-tech industries share in the manufacturing industry. This led to a decrease in the pace and relative volumes of value added creation in Ukrainian industry in comparison with global and regional indicators. Based on the data of well-known world ratings (CIP, SDG), the most problematic areas of the national industry were identified in the context of solving priority tasks of long-term development in conditions of accelerating technological transformations. Strategic directions are proposed that should fall into the focus of a long-term state industrial policy – structural development, stable internal demand, balanced exports.

More than a Feeling: Accuracy and Application of Sentiment Analysis

Sentiment is fundamental to human communication. Countless marketing applications mine opinions from social media communication, news articles, customer feedback, or corporate communication. Various sentiment analysis methods are available and new ones have recently been proposed. Lexicons can relate individual words and expressions to sentiment scores. In contrast, machine learning methods are more complex to interpret, but promise higher accuracy, i.e., fewer false classifications. We propose an empirical framework and quantify these trade-offs for different types of research questions, data characteristics, and analytical resources to enable informed method decisions contingent on the application context. Based on a meta-analysis of 272 datasets and 12 million sentiment-labeled text documents, we find that the recently proposed transfer learning models indeed perform best, but can perform worse than popular leaderboard benchmarks suggest. We quantify the accuracy-interpretability trade-off, showing that, compared to widely established lexicons, transfer learning models on average classify more than 20 percentage points more documents correctly. To form realistic performance expectations, additional context variables, most importantly the desired number of sentiment classes and the text length, should be taken into account. We provide a pre-trained sentiment analysis model (called SiEBERT) with open-source scripts that can be applied as easily as an off-the-shelf lexicon.