Метод межъязыкового аспектно-ориентированного анализа высказываний с использованием машинного обучение категоризационной модели.

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Today, the Internet has become the largest source of consumer thought. Sentiment analysis and opinion mining is the field of study that analyzes people’s opinions, sentiments, evaluations, attitudes, and emotions from written language. In this paper, we present a study of aspect-based opinion mining using a lexicon-based approach and their adaptation to the processing of responses written in Ukrainian and English. This information helps to build systems to understand customer’s feedback and plan business strategies accordingly. This also helps in predicting the chances of product failure. In this paper, it is explained how machine learning can be used for opinion mining. The research methods used in the work are based on data mining methods, Web mining, machine learning, and information retrieval. The stages of the method of cross-language aspect-oriented analysis of statements are presented. The cross-language categorization of characteristics of goods is considered. The algorithm describes the model learning in cross-language virtual contextual documents.Відгуки про продукцію є головним джерелом інформації для клієнтів і виробників, щоб допомогти їм прийняти відповідні рішення щодо закупівель і виробництва. Сьогодні Інтернет став найбільшим джерелом споживчої думки. Аналіз настроїв і видобування думок є сферою дослідження, яка аналізує думки людей, почуття, оцінки, ставлення та емоції з природно-мовного тексту. У даній роботі представлено дослідження аспектно-орієнтованого видобування думок з використанням лексіконного підходу та його адаптація до обробки відповідей, написаних українською та англійською мовами. Ця інформація допомагає створювати системи для розуміння зворотного зв'язку клієнта та планування відповідних бізнес-стратегій. Це також допомагає прогнозувати шляхи запобігання невдач при просуванні на ринку продуктів. У цій роботі розглянуто використання машинного навчання для видобутку думок клієнтів. Методи дослідження, що використовуються в роботі, базуються на методах інтелектуального аналізу даних, веб-добуванні, машинному навчанні та пошуку інформації. Представлено етапи методу міжмовного аспектно-орієнтованого аналізу тверджень. Розглянуто перехресну категоризацію характеристик товарів. Алгоритм описує модель навчання на міжмовному віртуальному контекстному документі.Отзывы о продукции является главным источником информации для клиентов и производителей, чтобы помочь им принять соответствующие решения в части закупок и производства. Сегодня Интернет стал крупнейшим источником потребительского мнения. Анализ настроений и выявления мыслей является сферой исследования, которая анализирует мнения людей, чувства, оценки, отношения и эмоции с естественно-языкового текста. В данной работе представлено исследование аспектно-ориентированного выявления мыслей с использованием лексиконного подхода и его адаптация к обработки ответов, написанных на украинском и английском языках. Эта информация помогает создавать системы для понимания обратной связи клиента и планирования соответствующих бизнес-стратегий. Это также помогает прогнозировать пути предотвращения неудач при продвижении на рынке продуктов. В этой работе рассмотрено использование машинного обучения для выявления мнений клиентов. Методы исследования, используемые в работе, базируются на методах интеллектуального анализа данных, веб-добывании, машинном обучении и поиска информации. Представлены этапы метода межъязыкового аспектно-ориентированного анализа утверждений. Рассмотрена перекрестная категоризацию характеристик товаров. Алгоритм описывает модель обучения на межъязыковой виртуальном контекстном документе

Reconsidering the calculation and role of environmental footprints

Following the recent Copenhagen Climate Change conference, there has been discussion of the methods and underlying principles that inform climate change targets. Climate change targets following the Kyoto Protocol are broadly based on a production accounting principle (PAP). This approach focuses on emissions produced within given geographical boundaries. An alternative approach is a consumption accounting principle (CAP), where the focus is on emissions produced globally to meet consumption demand within the national (or regional) economy1. Increasingly popular environmental footprint measures, including ecological and carbon footprints, attempt to measure environmental impacts based on CAP methods. The perception that human consumption decisions lie at the heart of the climate change problem is the impetus driving pressure on policymakers for a more widespread use of CAP measures. At a global level of course, emissions accounted for under the production and consumption accounting principles would be equal. It is international trade that leads to differences in emissions under the two principles. This paper, the second in this special issue of the Fraser Commentary, examines how input-output accounting techniques may be applied to examine pollution generation under both of these accounting principles, focussing on waste and carbon generation in the Welsh economy as a case study. However, we take a different focus, arguing that the ‘domestic technology assumption’, taken as something of a mid-point in moving between production and consumption accounting in the first paper, may actually constitute a more useful focus for regional policymakers than full footprint analyses

The electricity generation mix in Scotland : the long and windy road?

This article reports on research funded by the Engineering and Physical Sciences Research Council (EPSRC) at the University of Strathclyde

Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

With the rapid development of the World Wide Web, electronic word-of-mouth interaction has made consumers active participants. Nowadays, a large number of reviews posted by the consumers on the Web provide valuable information to other consumers. Such information is highly essential for decision making and hence popular among the internet users. This information is very valuable not only for prospective consumers to make decisions but also for businesses in predicting the success and sustainability. In this paper, a Gini Index based feature selection method with Support Vector Machine (SVM) classifier is proposed for sentiment classification for large movie review data set. The results show that our Gini Index method has better classification performance in terms of reduced error rate and accuracy

The Role of the Mangement Sciences in Research on Personalization

We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,

Social Data Mining for Crime Intelligence

With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems

Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

This work has been partially funded by projects PID2020-113462RB-I00 (ANIMALICOS), granted by Ministerio Espanol de Economia y Competitividad; projects P18-RT-4830 and A-TIC-608-UGR20 granted by Junta de Andalucia, and project B-TIC-402-UGR18 (FEDER and Junta de Andalucia).During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.Spanish Government PID2020-113462RB-I00Junta de Andalucia P18-RT-4830 A-TIC-608-UGR20 B-TIC-402-UGR18European Commission B-TIC-402-UGR1

Sustainable urban groundwater governance in Faisalabad, Pakistan: challenges and possibilities.

Groundwater use is high in developing countries, especially in places where municipal water authorities struggle to meet the water demand of their residents. To analyze the interactions between groundwater and piped water in the Global South context, Faisalabad, Pakistan was taken as the case study area. Using the Institutional Analysis and Development framework and Elinor Ostrom’s design principles as analysis tools, formal and informal institutions governing the piped water and groundwater, including their congruence with the social and ecological factors, were explored. The results showed that scarcity of piped water pushed people towards groundwater and the absence of informal and the weakness of formal governance rules allowed people to access groundwater without restrictions. As a result, urban groundwater in several parts of the city has depleted, while in others, it is close to depletion. According to Elinor Ostrom to manage the resources held in common; resource users can come together and devise institutions to govern the resource themselves. A one-shot common pool resource experiment was conducted with the household heads to determine if people want to want to come together and govern groundwater. The results of the game showed that participants are willing to moderately cooperate with each other on matters pertaining to groundwater withdrawal. As far as piped water is concerned, the residents’ choices for the piped water governance mode were also explored, using the discrete choice experiment and the conditional logit model. The results showed that people prefer to have a state-owned piped water system. In terms of governance, they prefer an impartial system and, to a lesser extent, prefer to co-produce piped water

The role of textual data in finance: methodological issues and empirical evidence

This thesis investigates the role of textual data in the financial field. Textual data fall into the more extensive category of alternative data. These types of data, such as reviews, blog post, tweet, are constantly growing, and this reinforces the importance in several domains. The thesis explores different applications of textual data in finance to highlight how it is possible to use this type of data and how this implementation can add value to financial analysis. The first application concerns the use of a lexicon-based approach in the credit scoring model. The second application proposes a causality detection between financial and sentiment data using an information-theoretic measure, the transfer entropy. The last application concerns the use of sentiment analysis in a network model, called BGVAR, to analyze the financial impact of the Covid-19 Pandemic. Overall, this thesis shows that combining textual data with traditional financial data can lead to a more insightful knowledge and, therefore, to a more in-depth analysis, allowing for a broader understanding of economic events and financial relationships among economic entities of any kind