A methodology for automatic derivation of cloud marketplace and cloud intelligence

University of Technology Sydney. Faculty of Engineering and Information Technology.From a consumer’s perspective, a cloud services marketplace is essential for cloud services discovery, selection, and composition. In practice, there are some private cloud services marketplaces, such as the Microsoft Azure marketplace, which are available for consumers belonging to a given vendor only. Nowadays, with the increase in the number of cloud services advertisements, and the adoption of cloud services, the cloud services consumer-base has grown and is projected to expand significantly over time. This increase defines the need for cloud services marketplace to enable effective interaction with cloud services users. A considerable amount of research has conducted in the area of cloud service selection and composition; however, the majority of this research is focused on developing algorithms (such as matching algorithms) and assumes the availability of cloud service information. Furthermore, little attention was given to the efficient discovery of cloud services over the World Wide Web (WWW). According to our literature, no research addresses the need for cloud services marketplace. Hence, this thesis proposes to provide an automatic derivation of cloud marketplace. The design of this marketplace includes a combination of the following modules: 1) cloud services harvesting module; 2) knowledge base for cloud service module; 3) cloud service trust derived intelligence module. The cloud services harvesting method is designed for harvesting cloud services advertisements from the web and building cloud services dataset. Such a dataset could be used by potential consumers for cloud services discovery and could be useful for future research in cloud selection, composition and recommender systems. Also, the developed cloud services repository could act as a knowledge source for constructing a standard ontology for cloud services. The knowledge base for cloud service module is designed for producing a solution toward cloud services marketplace to organise, publish and retrieve cloud services advertisements. This method involves semantically categories cloud services advertisements grounded on harvested web data to solve the issue of various cloud services advertisements. Also, this method includes the construction of the first commercial cloud services ontology-based repository for cloud services marketing. This repository contains service metadata that can be used to store service advertisements information which annotating to the domain-specific ontology concepts toward retrieving service advertisements more efficiently. The cloud services trust derived cloud Intelligence Module is designed to automatically analyzing the sentiment of cloud reviews to provide the potential consumers with real quality of service (Quality of Experience) information when making the buying decision. Also, building cloud reviews classifier to automatically classify the reviews: positive, neutral or negative using supervised machine learning algorithms. The result of this thesis will be an intelligent methodology for an automated derivation of the cloud marketplace: cloud services harvester, cloud services knowledge base, and Quality of Experience of cloud services. This methodology will be useful to the potential consumers, cloud providers, and the research community, as it will provide easy access to cloud services advertisements information

Modelos de aprendizaje supervisado como apoyo a la toma de decisiones en las organizaciones basados en datos de redes sociales: Una revisión sistemática de la literatura.

Las redes sociales se han convertido en la herramienta de comunicación e interacción más utilizada entre las personas y se han diversificado para cumplir funciones importantes dentro de la organización. En consecuencia, las redes sociales se han vuelto una fuente inmensa de datos que son procesados a través de modelos de aprendizaje supervisado para producir información que sea competente para la toma de decisiones como la predicción de campañas electorales, la predicción de consumo de un producto y/o servicio, la reputación de una empresa entre otros. De manera que el presente estudio tiene como objetivo identificar los modelos de aprendizaje supervisado como apoyo a la toma de decisiones en las organizaciones basados en datos de redes sociales. Para la identificación de modelos de aprendizaje supervisado se realizó una revisión sistemática de la literatura(RSL) en bases de datos reconocidas y revistas indexadas. De un total de 1614 artículos se identificaron 32 artículos que hacen referencia a 6 modelos de aprendizaje supervisado y las funciones que cumplen como apoyo a la toma de decisiones en una organización. Se puede concluir que existen diversos modelos de aprendizaje supervisado siendo el de Support Vector Machine de mayor grado de precisión. También se han encontrado en las investigaciones modelos de: Naive Bayes, Decision Tree, Regression: Logistic y lineal, k-Nearest Neighbors, y finalmente Neural Network.Trabajo de investigaciónLIMAEscuela Profesional de Ingeniería de SistemasTecnología de información e innovación tecnológic

CROSA: Context-aware cloud service ranking approach using online reviews based on sentiment analysis

[EN] The explosion of cloud services over the Internet has raised new challenges in cloud service selection and ranking. The existence of a great variety of offered cloud services made the users think deeply about the most appropriate services that meet their needs and at the same time are adaptable to their context. Nowadays, online reviews are used for the purpose of enhancing the effectiveness of finding useful product information, having impact on the consumers' decision-making process. In this context, the current paper suggests a context-aware cloud service ranking approach using online reviews and based on sentiment analysis (CROSA). Its main objective is to ease the cloud service selection. The CROSA approach analyzes sentiments associated with service measurement index (SMI)-based service properties for each alternative cloud service. Moreover, it enhances the cloud service decision-making by supporting fuzzy sentiments through the intuitionistic fuzzy set theory and PROMETHEE II. The experimental results presented in this paper show that this approach is efficient and performing.Ben-Abdallah, E.; Boukadi, K.; Lloret, J.; Hammami, M. (2021). CROSA: Context-aware cloud service ranking approach using online reviews based on sentiment analysis. Concurrency and Computation: Practice and Experience. 33(7):1-16. https://doi.org/10.1002/cpe.5358S11633

Sentiment Analysis for e-Payment Service Providers Using Evolutionary eXtreme Gradient Boosting

Online services depend primarily on customer feedback and communications. When this kind of input is lacking, the overall approach of the service provider can shift in unintended ways. These services rely on feedback to maintain consumer satisfaction. Online social networks are a rich source of consumer data related to services and products. Well developed methods like sentiment analysis can offer insightful analyses and aid service providers in predicting outcomes based on their reviews—which, in turn, enables decision-makers to develop effective strategic plans. However, gathering this data is more challenging on Arabic online social networks, due to the complexity of the Arabic language and its dialects. In this study, we propose an approach to sentiment analysis that combines a neutrality detector model with eXtreme Gradient Boosting and a genetic algorithm to effectively predict and analyze customers’ opinions of an e-Payment service through an Arabic social network. The proposed approach yields excellent results compared to other approaches. Feature analysis is also conducted on consumer reviews to identify influencing keywords.Deanship of Scientific Research, The University of JordanMinisterio espanol de Economia y Competitividad TIN2017-85727-C4-2-

Opinion mining in the evaluation of local restaurants: The case of Gaziantep

Turizm sektöründe, çevrimiçi turist yorumlarının analizi, işletmelerin sunduğu ürün ve hizmetleri değerlendirme ve turistlerin ihtiyaçlarını anlama yöntemlerinden birisi olarak görülmektedir. Turist yorumlarındaki metinsel içeriklerin değerlendirmesi, metin madenciliği yöntemlerinden fikir madenciliği ile yapılabilmektedir. Çalışmanın amacı; Gaziantep’te bölgeye özgü yemekler sunan ve TripAdvisor sitesinde yer alan restoranlara yönelik yabancı turist yorumlarının metin madenciliği yöntemlerinden, bakış tabanlı duygu analizi kullanılarak değerlendirilmesidir. Veri toplama aşamasında, Gaziantep ’de faaliyet gösteren ve TripAdvisor sitesinde ilk sekiz sırada yer alan restoranlar seçilmiştir. Araştırma kapsamında, sekiz restoranla ilgili 2019-2020 yıllarında yabancı turistlerin oluşturduğu 358 yorum, 05.01.2021-09.01.2021 tarihleri arasında araştırmacı tarafından web kazıma tekniği kullanılarak toplanmıştır. Araştırma bulgularına göre; yabancı turistler, Gaziantep restoranlarında sunulan yiyecekleri lezzetli bulmuşlar, taze ve acılı-baharatlı olmalarını olumlu değerlendirmişlerdir. Ayrıca restoranların ortamlarından ve çalışanların arkadaş canlısı yaklaşımlarından memnuniyetlerini ifade etmişlerdir. Turistler, restoranları temiz olarak değerlendirmişler ve başkalarına önerebileceklerini ifade etmişlerdir. Öte yandan turistler, restoranları pahalı ve yoğun oluşunu olumsuz olarak değerlendirmişlerdir.In the tourism industry, the analysis of online tourist comments is seen as one of the methods to evaluate the products and services offered by businesses and to understand the needs of tourists. Evaluation of textual contents in tourist comments can be done by opinion mining, one of the text mining methods. Purpose of the study; It is the evaluation of foreign tourists' comments about restaurants that serve Gaziantep region-specific dishes on the TripAdvisor site, using the aspect-based sentiment analysis technique. During the data collection phase, restaurants operating in Gaziantep and ranked in the top eight on the TripAdvisor site were selected. Within the scope of the research, 358 comments created by foreign tourists in 2019-2020 about eight restaurants were collected by the researcher using the web scraping technique between 05.01.2021-09.01.2021. According to the research findings; Foreign tourists found the food served in Gaziantep restaurants delicious and positively evaluated their freshness and spicy taste. They also expressed their satisfaction with the atmosphere of the restaurants and the friendly approach of the staff. Tourists evaluated the restaurants as clean and stated that they could recommend it to others. On the other hand, tourists negatively rated restaurants as being expensive and bus