Fotis Aisopos

Content vs. context for sentiment analysis: a comparative analysis over microblogs

Microblog content poses serious challenges to the applicability of traditional sentiment analysis and classification methods, due to its inherent characteristics. To tackle them, we introduce a method that relies on two orthogonal, but complementary sources of evidence: content-based features captured by n-gram graphs and context-based ones captured by polarity ratio. Both are language-neutral and noise-tolerant, guaranteeing high effectiveness and robustness in the settings we are considering. To ensure our approach can be integrated into practical applications with large volumes of data, we also aim at enhancing its time efficiency: we propose alternative sets of features with low extraction cost, explore dimensionality reduction and discretization techniques and experiment with multiple classification algorithms. We then evaluate our methods over a large, real-world data set extracted from Twitter, with the outcomes indicating significant improvements over the traditional techniques.

A recommender mechanism for service selection in service-oriented environments

The emergence of service-oriented computing as a key-enabler of web applications and the subsequent increase in the web services available, has brought to the surface a major disadvantage of service oriented architecture: there is no consistent way for the consumer to select services based on non-functional, quality requirements. Consumers perceive quality through the prism of their own experience and it is important to see how their evaluation of the quality provided is mapped to the specific quality parameters offered by the provider. In order to achieve that, this work suggests to use consumer ratings of the latter parameters so as to create a consumer quality profile and a provider reputation. By correlating this profile with others, it is possible to identify similarities in the service ratings that will lead to a prediction of which service might be the most appropriate for a specific consumer. Based on this rationale, we devised a Service Recommender mechanism and introduced a slight modification in the service lifecycle to accommodate the new Service Recommendation protocol that supports the mechanism.

Sentiment analysis of social media content using N-Gram graphs

Sentiment Analysis over Social Media facilitates the extraction of useful conclusions about the average public opinion on a variety of topics, but poses serious technical challenges. This is because of the sparse, noisy, multilingual content that is posted on-line by Social Media users. In this paper, we introduce a novel method for capturing textual patterns that inherently supports this challenging type of content. In essence, it creates a graph whose nodes correspond to the character n-grams of a document, while its weighted edges denote the average distance between them. Multiple documents of the same polarity can be aggregated into a polarity class graph, which can be compared with individual documents in order to identify the category of their sentiment. To evaluate our approach, we conducted large scale experiments on a real-world data set stemming from a snapshot of Twitter activity. The outcomes of our evaluation indicate significant improvements over other the methods typically used in this context, not only with respect to effectiveness, but also to efficiency.

Service selection decision support in the internet of services

With the emergence of service provisioning infrastructures and the networking capabilities of the web the IT world has been enhanced with the dynamics to support a market of services, where the number of providers and customers is potentially unbounded, also known as Internet of Services. In that frame, the customer comes against the problem of selecting a service from a plethora of available ones. This paper advocates that the large number of customers can provide the solution to this problem, by exploiting the experience of those that present similar behavior when they are asked to rate a provided service.

Textual and contextual patterns for sentiment analysis over microblogs

Microblog content poses serious challenges to the applicability of sentiment analysis, due to its inherent characteristics. We introduce a novel method relying on content-based and context-based features, guaranteeing high effectiveness and robustness in the settings we are considering. The evaluation of our methods over a large Twitter data set indicates significant improvements over the traditional techniques.

Effective QoS Monitoring in Large Scale Social Networks

Social Networking activities are still occupying the majority of the time that Internet users are spending in the Web. The generated content and social dynamics represent precious resources that everybody wishes to control. This scenario poses several challenges including the fact that different implementations, technologies, and formats are used to manage web content and social dynamics in heterogeneous, often antagonistic, Social Networking Sites. In order to master this heterogeneity the SocIoS project has defined an API that enables the aggregation of data and functionality made available by different Social Networking Sites APIs and their combination into complex and novel application workflows. However, the dependency on Social Networking Sites does not allow users of the SocIoS API to control the Quality of Service provided by the underlying platforms. In this paper we show how the QoSMONaaS (QoSMONitoring as a Service) component can be used to monitor and evaluate relevant metrics, such as availability and response time of the API calls, that are specified in the Service Level Agreement document. QoSMONaaS has been developed within the context of the SRT-15 project to implement a dependable (i.e. unbiased, reliable, and timely) monitoring of Quality of Service.

Social media meta-API: leveraging the content of social networks

Social Network (SN) environments are the ideal future service marketplaces. It is well known and documented that SN users are increasing at a tremendous pace. Taking advantage of these social dynamics as well as the vast volumes, of amateur content generated every second, is a major step towards creating a potentially huge market of services. In this paper, we describe the external web services that SocIoS project is researching and developing, and will support with the Social Media community. Aiming to support the end users of SNs, to enhance their transactions with more automated ways, and with the advantage for better production and performance in their workflows over SNs inputs and content, this work presents the main architecture, functionality, and benefits per external service. Finally, introduces the end user, into the new era of SNs with business applicability and better social transactions over SNs content.

Big IoT and Social Networking Data for Smart Cities

In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices, collected by smart city applications and socially-aware data aggregation services. A large set of city applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout participating cities is being applied, resulting into produced sets of millions of user-generated events and online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating algorithmic and configurations to minimize delays in dataset processing and results retrieval.

Policy Testing in Virtual Environments: Addressing Technical and Legal Challenges

Governments want to improve their policy making process by being able to accurately predict the impact of prospective policy measures to the community. Current e-government tools fail to capture the public opinion as they lack in mass participation. Instead of relying on outdated methods of communicating with the public, governments should embrace Web 2.0 technologies and take advantage of the vast the flows of information available online. In +Spaces, the authors introduce a novel way of accessing and evaluating public opinion by using popular virtual spaces, i.e., 3D Virtual Worlds and Social networks, as testing environments and developing an interface that would allow applications to operate inside them, capturing the reactions of citizens to prospective policies. They present the +Spaces platform giving emphasis on technical challenges such as Virtual Spaces interoperability as well as legal requirements related to processing user created data and how the authors addressed them.

A Toolkit Based Architecture for Optimizing Cloud Management, Performance Evaluation and Provider Selection Processes

Cloud environments are criticized for their volatility in performance aspects, making it extremely difficult for time- critical applications owners to perform the decisive step for migration and owners of SaaS to present performance vs cost tradeoffs to their customers when acting as IaaS customers. The aim of this work is to present an architectural approach based on which a)IaaS providers may enhance the stability and performance effectiveness of their infrastructures, through overhead modelling, runtime analysis and optimal groupings of concurrently running services, b) IaaS adopters to understand the application computational nature, investigate abstracted QoS metrics for providers ranking, minimize procurement time and selection processes, automate deployment/orchestration and monitor the maintenance of their SLA c) 3rd parties to act as independent validators of IaaS QoS features, through a constant monitoring and benchmarking process for performance and SLA evaluation.