Differentiating users by language and location estimation in sentiment analisys of informal text during major public events

In recent years there has been intense work on the analysis of social media to support marketing campaigns. A proper methodology for sentiment analysis is a crucial asset in this regard. However, when monitoring major public events the behaviour or social media users may be strongly biased by punctual actions of the participating characters and the sense of group belonging, which is typically linked to specific geographical areas. In this paper, we present a solution combining a location prediction methodology with an unsupervised technique for sentiment analysis to assess automatically the engagement of social network users in different countries during an event with worldwide impact. As far as the authors know, this is the first time such techniques are jointly considered. We demonstrate that the technique is coherent with the intrinsic disposition of individual users to typical actions of the characters participating in the events, as well as with the sense of group belonging.Ministerio de Economía, Industria y Competitividad | Ref. TEC2016-76465-C2-2-RXunta de Galicia | Ref. GRC2014/046Xunta de Galicia | Ref. ED341D R2016/01

Will the Home Team Win? On the Road to 1.5 Billion Tweets and Six Thousand Baseball Games Providing Insight!!!

Researchers operate with limited budgets and inadequate resources. This prohibits big data research and suppresses innovation needed to direct inquiry and construct robust research-based information systems. Such issues are not insuperable, e.g., this project is initialized with limited resources and attempts to build theory, describe architecture, and set the vision for future work. This first “On the Road to …” paper tenders a methodology that examines the use of social media variables as a proxy for human emotion and epistemic activity. A social media corpus is processed and a regression model considers MLB team wins as the dependent variable and a social media tweet corpus, operationalized via NLP, as the independent variable. Results are presented. Future work describes a predictive GIS artifact that will input, process, and visualize a spatial and time-based, NLP processed, social media corpus and is integration with geospatial indexing

A sentiment analysis software framework for the support of business information architecture in the tourist sector

In recent years, the increased use of digital tools within the Peruvian tourism industry has created a corresponding increase in revenues. However, both factors have caused increased competition in the sector that in turn puts pressure on small and medium enterprises' (SME) revenues and profitability. This study aims to apply neural network based sentiment analysis on social networks to generate a new information search channel that provides a global understanding of user trends and preferences in the tourism sector. A working data-analysis framework will be developed and integrated with tools from the cloud to allow a visual assessment of high probability outcomes based on historical data, to help SMEs estimate the number of tourists arriving and places they want to visit, so that they can generate desirable travel packages in advance, reduce logistics costs, increase sales, and ultimately improve both quality and precision of customer service

CORPORATE SOCIAL RESPONSIBILITY IN ROMANIA

The purpose of this paper is to identify the main opportunities and limitations of corporate social responsibility (CSR). The survey was defined with the aim to involve the highest possible number of relevant CSR topics and give the issue a more wholesome perspective. It provides a basis for further comprehension and deeper analyses of specific CSR areas. The conditions determining the success of CSR in Romania have been defined in the paper on the basis of the previously cumulative knowledge as well as the results of various researches. This paper provides knowledge which may be useful in the programs promoting CSR.Corporate social responsibility, Supportive policies, Romania

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality

Co-constructing a new framework for evaluating social innovation in marginalized rural areas

The EU funded H2020 project \u2018Social Innovation in Marginalised Rural Areas\u2019 (SIMRA; www.simra-h2020.eu) has the overall objective of advancing the state-of-the-art in social innovation. This paper outlines the process for co- developing an evaluation framework with stakeholders, drawn from across Europe and the Mediterranean area, in the fields of agriculture, forestry and rural development. Preliminary results show the importance of integrating process and outcome-oriented evaluations, and implementing participatory approaches in evaluation practice. They also raise critical issues related to the comparability of primary data in diverse regional contexts and highlight the need for mixed methods approaches in evaluation

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data