A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.Comment: 25 page

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Exploring key economic sectors and groups of sectors in Scotland; 1998, 2004, 2007

Different methods and criteria exist for determining ‘key’ economic sectors. The Scottish Government identifies a number of ‘key’ sectors, although it is not clear which metrics it used to choose these. It is likely that these sectors are considered to be ‘key’ in delivering the Scottish Government’s policy priorities. This differs from a more formally defined economic approach to determining key sectors. However, even within the economics literature, there are different ways of thinking about which sectors are ‘key’. This short paper presents one approach to determining individual and groups of ‘key’ sectors. We will explain why these approaches are not necessarily equivalent, and what value is added in moving from considering sectors individually to analysing the impact of sectors in groups. We begin with a non-technical overview of the methods we employ, before discussing the database used in this analysis. We then present the results of applying this method for Scotland for three time periods: 1998, 2004, and 2007. We mainly focus on sectoral output, but we also include one set of results which look at key employment sectors. In the discussion of our results we concentrate on two things. First, we are interested in which sectors are identified as important in Scotland in each time period. Second, we investigate how those sectors have changed between 1998, 2004 and 2007

Gamification in Proprietary Innovation: Identifying a Technical Framework Based on Patent Data.

This paper reveals dominant patterns of gamification in proprietary innovation and develops a technical framework. In recent years, a rash increase in securitizing gamification-related inventions has taken place. By analyzing the content of 134 unique patents from USPTO and EPO with an in-depth raw data text analysis, the technical background is explored holistically. To discover meaningful patterns and thus to derive implications from the patent data they are visually summarized. Especially predominant are the topics of device, data, user and game. Based on the nature of the data, being evidence-based and future directed, our technical framework integrates these patterns and sets it into relation. An additional analysis provides further insights into fundamental game elements. As patents serve as a decisive indicator of future product introductions, the information gathered in this paper represents essential strategic information to guide practitioners and researchers in the area of gamification

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Data DNA: The Next Generation of Statistical Metadata

Describes the components of a complete statistical metadata system and suggests ways to create and structure metadata for better access and understanding of data sets by diverse users