Text pre-processing of multilingual for sentiment analysis based on social network data

<span>Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text pre-processing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, and brand monitoring. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition.</span>

Helping as a Concurrent Activity: How Students Engage in Small Groups While Pursuing Classroom Tasks

This study examines interactions in which students help each other with their learning during classroom instruction, forming groups in the process. From a conversation analytic perspective, helping is assumed to be a sequentially organized activity jointly accomplished by the participants. As an activity that proceeds alongside other ongoing classroom activities, helping can be conceived as part of a multiactivity that poses students with multi-faceted interactional and moral challenges. While previous research on helping in educational contexts has primarily focused on the influence of helping on learning outcomes and social dynamics in helping interactions, the present study investigates how students cope with the intricacies of moral commitments inherent in helping as a concurrent activity. The aim of this paper is two-fold. First, we aim to elaborate on how students’ dual involvements – i.e., their involvement in classroom activities while simultaneously providing help – manifest in the ways in which groups are constituted, maintained, and dissolved. The analyses reveal that both the compatibility of helping with the activity already in progress as well as the students’ problem definition are consequential for the sequential and bodily-spatial unfolding of the help interaction, inducing different arrangements that constitute a continuum, at each end of which there is a dominant orientation toward the shared space of helping or toward the individual/collective space. Furthermore, from a methodological perspective, our study aims to demonstrate the extent to which multimodal interaction analysis is applicable when examining naturally occurring groups, in this case, in interactive processes of helping. The study is based on a data corpus that comprises video recordings of mathematics and German lessons from two fifth-grade classrooms.

Distilling the curriculum: An analysis of alcohol industry-funded school-based youth education programmes

Background and aim For decades, corporations such as the tobacco and fossil fuel industries have used youth education programmes and schools to disseminate discourses, ideas and values favourable to their positions, and to pre-empt regulation that threatens profits. However, there is no systematic research into alcohol industry-funded youth education programmes. This article serves to address this important gap in the literature. Methods Using a discourse theoretical approach informed by poststructural discourse theory and critical discourse analysis, we analysed teaching materials from three school-based youth education initiatives which focus on alcohol consumption and health harms: Drinkaware for Education, The Smashed Project (funded by Diageo), and Talk About Alcohol (Alcohol Education Trust). These materials, some of which are disseminated internationally, are provided to schools through intermediary bodies in receipt of alcohol industry funding. Findings The analysis found that these materials drew from and presented discourses of personal responsibility, moderate alcohol consumption, and involved a narrowing of the problem definition and causes. The locus of the problem is located by the discourses within individuals including youth, with causes of youth alcohol consumption repeatedly presented as peer pressure and ‘poor choices’, with little or no mention of alcohol industry marketing or other practices. All programmes promoted familiarisation and normalisation of alcohol as a ‘normal’ adult consumer product which children must learn about and master how to use responsibly when older. The discourses constructed in these materials closely align with those of other alcohol industry corporate social responsibility discourses which employ selective presentation of harms, including misinformation about cancer, and ambiguous terms such as “responsible drinking”. Furthermore, the role of alcohol price, availability and access, and the impacts of alcohol and the industry on inequities were not articulated within the discourses. The research was limited to an analysis of teaching materials and further research is needed to explore their impact on youth, teachers and wider discourses and social norms. Conclusion Alcohol industry-sponsored youth education programmes serve industry interests and promote moderate consumption while purportedly educating children about harms and influences of alcohol use. There are considerable conflicts of interest in the delivery of alcohol education programmes funded by the alcohol industry and intermediary bodies in receipt of such funding. Alcohol education materials should be developed independent from industry, including funding, and should empower children and young people to understand and think critically about alcohol, including harms and drivers of consumption, and effective interventions needed to protect them and others from alcohol-related harms. Independent organisations can use this analysis to critique their materials to strengthen alignment with meeting student and public health interests. The ongoing exposure of children and young people to such conflicted and misleading materials needs urgent attention from policymakers, practitioners, teachers and parents, and resources dependent on industry support should cease being used in schools.

Logistics Performance, Ratings, and Its Impact on Customer Purchasing Behavior and Sales in E-Commerce Platforms

Problem definition: We examine the impact of logistics performance metrics such as delivery time and customer’s requested delivery speed on logistics service ratings and third-party sellers’ sales on an e-commerce platform. Academic/practical relevance: Although e-commerce retailers like Amazon have recently invested heavily in their logistics networks to provide faster delivery to customers, there is scant academic literature that tests and quantifies the premise that convenient and fast delivery will drive sales. In this paper, we provide empirical evidence on whether this relationship holds in practice by analyzing a mechanism that connects delivery performance to sales through logistics ratings. Prior academic work on online ratings in e-commerce platforms has mostly analyzed customers’ response to product functional performance and biases that exist within. Our study contributes to this stream of literature by examining customer experience from a service quality perspective by analyzing logistics service performance, logistics ratings, and its impact on customer purchase probability and sales. Methodology: Using an extensive data set of more than 15 million customer orders on the Tmall platform and Cainiao network (logistics arm of Alibaba), we use the Heckman ordered regression model to explain the variation in customers’ rating of logistics performance and the likelihood of customers posting a logistics rating. Next, we develop a generic customer choice model that links the customer’s likelihood of making a purchase to the logistics ratings provided by prior customers. We implement a two-step estimation of the choice model to quantify the impact of logistics ratings on customer purchase probability and third-party seller sales. Results: We surprisingly find that even customers with no promise on delivery speed are likely to post lower logistics ratings for delivery times longer than two days. Although these customers are not promised an explicit delivery deadline, they seem to have a mental threshold of two days and expect deliveries to be made within that time. Similarly, we find that priority customers (those with two-day and one-day promise speed) provide lower logistics ratings for delivery times longer than their anticipated delivery date. We estimate that reducing the delivery time of all three-day delivered orders on this platform (which makeup [Formula: see text] 35% of the total orders) to two days would improve the average daily third-party seller sales by 13.3% on this platform. The impact of delivery time performance on sales is more significant for sellers with a higher percentage of three-day delivered orders and a higher spend per order. Managerial implications: Our study emphasizes that delivery performance and logistics ratings, which measure service quality, are essential drivers of the customer purchase decision on e-commerce platforms. Furthermore, by quantifying the impact of delivery time performance on sales, our study also provides a framework for online retailers to assess if the increase in sales because of improved logistics performance can offset the increase in additional infrastructure costs required for faster deliveries. Our study’s insights are relevant to third-party sellers and e-commerce platform managers who aim to improve long-term online customer traffic and sales.

Ride-Hailing Platforms: Competition and Autonomous Vehicles

Problem definition: Ride-hailing platforms, which are currently struggling with profitability, view autonomous vehicles (AVs) as important to their long-term profitability and prospects. Are competing platforms helped or harmed by platforms’ obtaining access to AVs? Are the humans who participate on the platforms—driver-workers and rider-consumers (hereafter, agents)—collectively helped or harmed by the platforms’ access to AVs? How do the conditions under which access to AVs reduces platform profits, agent welfare, and social welfare depend on the AV ownership structure (i.e., whether platforms or individuals own AVs)? Academic/practical relevance: AVs have the potential to transform the economics of ride-hailing, with welfare consequences for platforms, agents, and society. Methodology: We employ a game-theoretic model that captures platforms’ price, wage, and AV fleet size decisions. Results: We characterize necessary and sufficient conditions under which platforms’ access to AVs reduces platform profit, agent welfare, and social welfare. The structural effect of access to AVs on agent welfare is robust regardless of AV ownership; agent welfare decreases if and only if the AV cost is high. In contrast, the structural effect of access to AVs on platform profit depends on who owns AVs. The necessary and sufficient condition under which access to AVs decreases platform profit is high AV cost under platform-owned AVs and low AV cost under individually owned AVs. Similarly, the structural effect of access to AVs on social welfare depends on who owns AVs. Access to individually owned AVs increases social welfare; in contrast, access to platform-owned AVs decreases social welfare—if and only if the AV cost is high. Managerial implications: Our results provide guidance to platforms, labor and consumer advocates, and governmental entities regarding regulatory and public policy decisions affecting the ease with which platforms obtain access to AVs.

Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment

Traditional intent recognition algorithms of intelligent prosthesis often use deep learning technology. However, deep learning’s high accuracy comes at the expense of high computational and energy consumption requirements. Mobile edge computing is a viable solution to meet the high computation and real-time execution requirements of deep learning algorithm on mobile device. In this paper, we consider the computation offloading problem of multiple heterogeneous edge servers in intelligent prosthesis scenario. Firstly, we present the problem definition and the detail design of MEC-based task offloading model for deep neural network. Then, considering the mobility of amputees, the mobility-aware energy consumption model and latency model are proposed. By deploying the deep learning-based motion intent recognition algorithm on intelligent prosthesis in a real-world MEC environment, the effectiveness of the task offloading and scheduling strategy is demonstrated. The experimental results show that the proposed algorithms can always find the optimal task offloading and scheduling decision.

Mobile Payment Services, Government Involvement, and Mobile Network Operator Performance

Problem definition: Recent developments in mobile payment services (MPS) have shown an increasing role of mobile-government (m-government) initiatives in improving the market performance of mobile network operators (MNOs) and financial inclusion. High costs and operational challenges have discouraged MNOs from fully committing to the development of MPS, but government involvement under m-government may increase MNO user bases by providing the scale and scope necessary to incentivize MNOs. Academic/practical relevance: Extant research on mobile payment has ignored the role of governments as important stakeholders in the mobile financial ecosystem. Our research contributes to the literature by examining the role of governments as business partners in MPS launches and the effect of government involvement on MNO user bases. Methodology: Using a unique proprietary data set from the mobile network industry, we design a quasi-experiment to examine the causal effects of government involvement in MPS on MNOs’ total mobile connections. More importantly, we adopt a changes-in-changes (CIC) estimation approach to further establish nonlinear treatment effects of government involvement based on MNO size and MPS type. Results: We find that government involvement expands MNO user bases beyond MPS launches. Such effects increase with MNO size and MPS variety, favoring larger MNOs and, to a certain degree, MNOs with diverse offerings of government-involved MPS. Government involvement in MPS launches also directly benefits MNOs with microloan services. In addition, government regulations and policies to encourage financial inclusion can also expand MNO user bases. Managerial implications: Governments play a critical role in promoting technologies and financial services both as a regulator and as a business partner. To improve market performance, MNOs should take advantage of the scale and scope of government services by partnering with government agencies in launching MPS. MNOs should also embrace government policies and regulations to increase user bases.

Problem Definition

This chapter begins with the Socrates DigitalTM module calling the “Define Problem” process. This process identifies the problem area and gathers the problem-defining information from the user. This chapter provides pseudo-code for the subprocesses that make up the processes for Socrates DigitalTM. It has enough detail to implement the logic in any procedural and general-purpose computer programming language. This chapter shows that more questions follow after asking the user a question in many situations. The questions aimed at getting these answers are questions that target the quality of reasoning.

UNESCO, Idanha-a-Nova, and the UNESCO Creative Cities Network

The chapter's main objective is to study the city's rising role as a driver for implementing the 2030 SDGs and UNESCO Creative Cities Network's part as UNESCO's mechanism to support cities in the effort. The results show that there is a changing nature of authority in the policy cycle on a more holistic level, where alongside the nation-state, international organizations and cities play a vital role in the problem definition, decision-making, agenda-setting, transfer, and implementation of policies. The increasing importance of cities internationally stretches the municipal policy cycle from the local to regional, national, and international levels. Orchestration complemented with an inter-organizational relations framework is used to study the case of Idanha-a-Nova UNESCO Creative City of Music. The case study shows that Idanha-a-Nova drove the implementation of the SDGs locally with the Portuguese state's support. However, because it lacked expertise and mechanisms of implementing the goals, it reached out to private consultancy and individual experts.

Language consequences of modern psychological and information warfare in Russian mass-media: problem definition

The paper deals with the urgent and topical issue of political linguistics - the influence of information and psychological warfare on the Russian language. The aim of the paper is to describe the most frequent novices in the modern Russian language and speech which occur due to the domestic information and psychological warfare. The research was carried out on the basis of the mass-media texts, the traditional linguistic research methods were used (analysis and description, contextual and axiological analysis, etc.). As the result of the analysis the authors singled out both new and traditional words and word combinations which simultaneously serve as the weapon and the result of information and psychological warfare. Two groups of language (speech) means were defined: specialized (which perform the relevant evaluative function - either positive or negative) and non-specialized (which change the function depending on the context, the semantic ambivalent words and word combinations). The specialized means include pejorative words and word combinations: political labels, invectives, terribilitisms (bogey-words), delusions (trap-words), negatively connotative words, and euphemisms. Ameliorative means are not characteristic of information and psychological warfare, though words and word combinations are widely used which denote national concepts being the subject of information rivalry. Neutral language means in information and psychological warfare in the Russian language include terms and terminoids, naming various types of rivalries and technologies constituting them. The results obtained contribute to the development of the information and psychological warfare linguistics. Research perspectives encompass the refinement of some points and the analysis of information and psychological warfare language consequences in the light of linguistic ecology.