Meta-Information as a Service: A Big Social Data Analysis Framework

Social information services generate a large amount of data. Traditional social information service analysis techniques first require the large data to be stored, and afterwards processed and analyzed. However, as the size of the data grows the storing and processing cost increases. In this paper, we propose a ‘Meta-Information as a Service’ (MIaaS) framework that extracts the data from various social information services and transforms into useful information. The framework provides a new formal model to present the services required for social information service data analysis. An efficient data model to store and access the information. We also propose a new Quality of Service (QoS) model to capture the dynamic features of social information services. We use social information service based sentiment analysis as a motivating scenario. Experiments are conducted on real dataset. The preliminary results prove the feasibility of the proposed approach

Big Social Data Analytics: A Model for the Public Sector

The influence of Information and Communication Technologies (ICTs) particularly internet technology has had a fundamental impact on the way government is administered, provides services and interacts with citizens. Currently, the use of social media is no longer limited to informal environments but is an increasingly important medium of communication between citizens and governments. The extensive and increasing use of social media will continue to generate huge amounts of user-generated content known as Big Social Data (BSD). The growing body of BSD presents innumerable opportunities as well as challenges for local government planning, management and delivery of public services to citizens. However, the governments have not yet utilised the potential of BSD for better understanding the public and gaining new insights from this new way of interactions. Some of the reasons are lacking in the mechanism and guidance to analyse this new format of data. Thus, the aim of this study is to evaluate how the body of BSD can be mined, analysed and applied in the context of local government in the UK. The objective is to develop a Big Social Data Analytics (BSDA) model that can be applied in the case of local government. Data generated from social media over a year were collected, collated and analysed using a range of social media analytics and network analysis tools and techniques. The final BSDA model was applied to a local council case to evaluate its impact in real practice. This study allows to better understand the methods of analysing the BSD in the public sector and extend the literature related to e-government, social media, and social network theoryUniversiti Utara Malaysi

Sentiment analysis as a service

This research focuses on the design and development of a service composition based framework that enables the execution of services for social media based sentiment analysis. Our research develops novel analytical models, composition techniques and algorithms which use services as a mean for sentiment abstraction, processing and analysis from large scale social media data. Current sentiment analysis techniques require specialized skill of data science and machine learning. Moreover, traditional approaches rely on laborious and time-consuming activities such as manual dataset labelling, data model training and validation. This makes overall sentiment analysis process a challenging task. In comparison, services are `ready-made' software solutions that can be composed on-demand for developing complex applications without indulging in the domain specific details. This thesis investigates a novel approach that transforms traditional social media based sentiment analysis process into a service composition driven solution. In this thesis, we begin by developing a novel service framework that replaces the traditional sentiment analysis tasks with online services. Our framework includes a new service model to present services required for sentiment analysis. We develop a semantic service composition model and algorithm that dynamically composes various services for data collection, noise filtering and sentiment extraction. In particular, we focus on abstracting sentiment based on location and time. We then focus on enhancing the flexibility of our proposed service framework to compose appropriate sentiment analysis services for highly dynamic and changing features of social media platforms. In addition, we aim to efficiently process and analyze large scale social media data. In order to enhance our service composition framework, we propose a novel approach to formalize social media platforms as cloud enabled services. We develop a functional and quality of service (QoS) model that captures various dynamic features of social media platforms. In addition, we devise a cloud based service model to access social media platforms as services by using the Ontology Web Language for Service (OWL-S). Secondly, we integrate the QoS model into our existing composition framework. It enables our framework to dynamically assess the QoS of multiple social media platforms, and simultaneously compose appropriate services to extract, process, analyze and integrate the sentiment results from large scale data. Finally, we concentrate on efficient utilization of the sentiment analysis extracted from large scale data. We formulate a meta-information composition model that transforms and stores sentiment obtained from large streams of data into re-usable information. Later, the re-usable information is on-demand integrated and delivered to end users. To demonstrate the performance and test the effectiveness of our proposed models, we develop prototypes to evaluate our composition framework. We design several scenarios and conduct a series of experiments using real-world social media datasets. We present the results and discuss the outcomes which validate the performance of our research

A theoretical model of social media monitoring capability : exploring its components and potential impact on organizational competitiveness

This study examined an organizational capability that involves extracting external intelligence from social media platforms to inform business decision-making. The thesis offered several contributions by developing a model of social media monitoring capability, its components and potential impact on organizational competitiveness