Improved lexicon-based sentiment analysis for social media analytics

Social media channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public attitude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for different organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key components, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classification process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events.

Twitter Sentiment Analysis for Security-Related Information Gathering

Analysing public sentiment about future events, such as demonstration or parades, may provide valuable information while estimating the level of disruption and disorder during these events. Social media, such as Twitter or Facebook, provides views and opinions of users related to any public topics. Consequently, sentiment analysis of social media content may be of interest to different public sector organisations, especially in the security and law enforcement sector. In this paper we present a lexicon-based approach to sentiment analysis of Twitter content. The algorithm performs normalisation of the sentiment in an effort to provide intensity of the sentiment rather than positive/negative label. Following this, we evaluate an evidence-based combining function that supports the classification process in cases when positive and negative words co-occur in a tweet. Finally, we illustrate a case study examining the relation between sentiment of twitter posts related to English Defence League and the level of disorder during the EDL related events.

Business Intelligence and Multi-market Competition

We consider a multi-market framework where a set of firms compete on two oligopolistic markets. The cost of production of each firm allows for spillovers across markets, ensuring that output decisions for both markets have to be made jointly. Prior to competing in these markets, firms can establish links gathering business intelligence about other firms. A link formed by a firm generates two types of externalities for competitors and consumers. We characterize the business intelligence equilibrium networks and networks that maximize social welfare. By contrast with single market competition, we show that in multi-market competition there exist situations where intelligence gathering activities are underdeveloped with regard to social welfare and should be tolerated, if not encouraged, by public authorities.

A lesson in business: cost-effectiveness analysis of a novel financial incentive intervention for increasing physical activity in the workplace

Background: Recently both the UK and US governments have advocated the use of financial incentives to encourage healthier lifestyle choices but evidence for the cost-effectiveness of such interventions is lacking. Our aim was to perform a cost-effectiveness analysis (CEA) of a quasi-experimental trial, exploring the use of financial incentives to increase employee physical activity levels, from a healthcare and employer’s perspective.

Methods: Employees used a ‘loyalty card’ to objectively monitor their physical activity at work over 12 weeks. The Incentive Group (n=199) collected points and received rewards for minutes of physical activity completed. The No Incentive Group (n=207) self-monitored their physical activity only. Quality of life (QOL) and absenteeism were assessed at baseline and 6 months follow-up. QOL scores were also converted into productivity estimates using a validated algorithm. The additional costs of the Incentive Group were divided by the additional quality adjusted life years (QALYs) or productivity gained to calculate incremental cost effectiveness ratios (ICERs). Cost-effectiveness acceptability curves (CEACs) and population expected value of perfect information (EVPI) was used to characterize and value the uncertainty in our estimates.

Results: The Incentive Group performed more physical activity over 12 weeks and by 6 months had achieved greater gains in QOL and productivity, although these mean differences were not statistically significant. The ICERs were £2,900/QALY and £2,700 per percentage increase in overall employee productivity. Whilst the confidence intervals surrounding these ICERs were wide, CEACs showed a high chance of the intervention being cost-effective at low willingness-to-pay (WTP) thresholds.

Conclusions: The Physical Activity Loyalty card (PAL) scheme is potentially cost-effective from both a healthcare and employer’s perspective but further research is warranted to reduce uncertainty in our results. It is based on a sustainable “business model” which should become more cost-effective as it is delivered to more participants and can be adapted to suit other health behaviors and settings. This comes at a time when both UK and US governments are encouraging business involvement in tackling public health challenges.

Using Brain Data for Sentiment Analysis

We present the results of exploratory experiments using lexical valence extracted from brain using electroencephalography (EEG) for sentiment analysis. We selected 78 English words (36 for training and 42 for testing), presented as stimuli to 3 English native speakers. EEG signals were recorded from the subjects while they performed a mental imaging task for each word stimulus. Wavelet decomposition was employed to extract EEG features from the time-frequency domain. The extracted features were used as inputs to a sparse multinomial logistic regression (SMLR) classifier for valence classification, after univariate ANOVA feature selection. After mapping EEG signals to sentiment valences, we exploited the lexical polarity extracted from brain data for the prediction of the valence of 12 sentences taken from the SemEval-2007 shared task, and compared it against existing lexical resources.

Personalising Crime and Crime-fighting in Factual Television:an Analysis of Social Actors and Transitivity in Language and Images

This article addresses the lack of work on media and crime in Critical Discourse Analysis (CDA), using an example of a factual television crime report. The existing research in media studies and criminology points to the way that the media misrepresents crime by distorting public understandings and backgrounding structural issues, such as poverty, which are related to crime thereby legitimising a criminal justice system that serves the interests of the powerful in society. Using social actor and transitivity analysis, this article shows how multimodal CDA can make an important contribution as it reveals the more subtle linguistic strategies and visual representations by which this process is accomplished, showing how each plays a part in the recontextualisation of social practice. This programme backgrounds which crimes are committed but foregrounds mental states and the neutrality of policing.

Work-place social capital and smoking cessation: The Finnish Public Sector Study

AIMS:
To examine whether high social capital at work is associated with an increased likelihood of smoking cessation in baseline smokers.
DESIGN:
Prospective cohort study.
SETTING:
Finland.
PARTICIPANTS:
A total of 4853 employees who reported to be smokers in the baseline survey in 2000-2002 (response rate 68%) and responded to a follow-up survey on smoking status in 2004-2005 (response rate 77%).
MEASUREMENTS:
Work-place social capital was assessed using a validated and psychometrically tested eight-item measure. Control variables included sex, age, socio-economic position, marital status, place of work, heavy drinking, physical activity, body mass index and physician-diagnosed depression.
FINDINGS:
In multi-level logistic regression models adjusted for all the covariates, the odds for being a non-smoker at follow-up were 1.26 [95% confidence interval (CI)=1.03-1.55] times higher for baseline smokers who reported high individual-level social capital than for their counterparts with low social capital. In an analysis stratified by socio-economic position, a significant association between individual-level social capital and smoking cessation was observed in the high socio-economic group [odds ratio (OR) (95% CI)=1.63 (1.01-2.63)], but not in intermediate [(OR=1.10 (0.83-1.47)] or low socio-economic groups [(OR=1.28 (0.86-1.91)]. Work unit-level social capital was not associated with smoking cessation.
CONCLUSIONS:
If the observed associations are causal, these findings suggest that high perceived social capital at work may facilitate smoking cessation among smokers in higher-status jobs.

Unveiling Fairness for the Consumer:The Law, Economics and Justice of Expanded Arbitration

In recent years, the US Supreme Court has rather controversially extended the ambit of the Federal Arbitration Act to extend arbitration’s reach into, inter alia¸ consumer matters, with the consequence that consumers are often (and unbeknownst to them) denied remedies which would otherwise be available. Such denied remedies include recourse to class action proceedings, effective denial of punitive damages, access to discovery and the ability to resolve the matter in a convenient forum.

The court’s extension of arbitration’s ambit is controversial. Attempts to overturn this extension have been made in Congress, but to no avail. In contrast to American law, European consumer law looks at pre-dispute agreements to arbitrate directed at consumers with extreme suspicion, and does so on the grounds of fairness. In contrast, some argue that pre-dispute agreements in consumer (and employment) matters are consumer welfare enhancing: they decrease the costs of doing business, which is then passed on to the consumer. This Article examines these latter claims from both an economic and normative perspective.

The economic analysis of these arguments shows that their assumptions do not hold. Rather than being productive of consumer surplus, the use of arbitration is likely to have the opposite effect. The industries from which the recent Supreme Court cases originated not only do not exhibit the industrial structure assumed by the proponents of expanded arbitration, but are also industries which exhibit features that facilitate consumer welfare reducing collusion.

The normative analysis addresses the fairness concerns. It is explicitly based upon John Rawls’ notion of “justice as fairness,” which can provide a lens to evaluate social institutions. This Rawlsian analysis considers the use of extended arbitration in consumer matters in the light of the earlier economic results. It suggests that the asymmetries present in the contractual allocation of rights serve as prima facie evidence that such arbitration–induced exclusions are prima facie unjust/unfair. However, as asymmetry is only a prima facie test, a generalized criticism of the arbitration exclusions (of the sort found in Congress and underlying the European regime) is overbroad.

Non-domination, non-alienation and social equality: towards a republican understanding of equality

The republican idea of non-domination stresses the importance of certain social relationships for a person’s freedom, showing that freedom is a social-relational state. While the idea of freedom as non-domination receives a lot of attention in the literature, republican theorists say surprisingly little about equality. Therefore, the aim of this paper is to carve out the contours of a republican conception of equality.. In so doing, I will argue that republican accounts of equality share a significant normative overlap with the idea of social equality. However, closer analysis of Philip Pettit’s account of ‘expressive egalitarianism’ (which Pettit sees as inherently connected to non-domination) and recent theories of social equality shows that republican non-domination – in contrast to what Pettit seems to claim – is not sufficient for securing (republican) social equality. In order to secure social equality for all, republicans would have to go beyond non-domination.

Advanced Business Simulations-Incorporating business and process execution data

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.

Qualitative Comparative Analysis as a Tool for Concept Clarification, Typology Building, and Contextualized Comparisons in Gender and Feminist Research

Qualitative Comparative Analysis (QCA) is a method for the systematic analysis of cases. A holistic view of cases and an approach to causality emphasizing complexity are some of its core features. Over the last decades, QCA has found application in many fields of the social sciences. In spite of this, its use in feminist research has been slower, and only recently QCA has been applied to topics related to social care, the political representation of women, and reproductive politics. In spite of the comparative turn in feminist studies, researchers still privilege qualitative methods, in particular case studies, and are often skeptical of quantitative techniques (Spierings 2012). These studies show that the meaning and measurement of many gender concepts differ across countries and that the factors leading to feminist success and failure are context specific. However, case study analyses struggle to systematically account for the ways in which these forces operate in different locations.

Recruitment and Retention in Front-line Services: The case of childcare

This article highlights how problems of recruitment and retention in front-line services create a particular challenge to traditional HRM models and solutions. Private day nurseries make an interesting example of the challenges facing managers in the service sector as the combination of a feminised workforce, a price-sensitive service, public-private competition and state regulation create particular difficulties. We report on a study of 33 day nurseries involving interviews with managers and employees over an eight-month period. Our findings show that childcare providers have to cope with recruitment and retention problems associated with high-end interactive service provision compounded by gender segregation and small business characteristics. Our analysis of employer and employee perspectives examines labour market issues affecting recruitment, and categorises the reasons for staff turnover into internal 'push' factors, external 'pull' factors, outside factors and functional turnover.

Understanding Parental Influence on Educational Outcomes Among 9 Year Olds in Ireland: The Mediating Role of Resources, Attitudes and Children's Own Perspectives

The relationship between parental background and children's educational outcomes has been a dominant theme within the sociology of education. There has been an on-going debate as to the relative merits of explanations which focus on the role of socio-cultural reproduction and those which focus on rational choice. However, many empirical studies within the social stratification tradition fail to allow for children's own agency in shaping the relationship between social background and schooling outcomes. This paper draws on the first wave of a large-scale longitudinal study of over 8,000 nine-year-old children in Ireland, which combines information from parents, school principals, teachers and children themselves. Both social class and parental education are found to have significant effects on reading and mathematics test scores among nine year olds. These effects are partly mediated by home-based educational resources and activities, parents' educational expectations for their child, and parents' formal involvement in the school. More importantly, children's own engagement with, and attitudes to, school significantly influence their academic performance. The influence of children's own attitudes and actions can thus reinforce or mitigate the effect of social background factors. The analysis therefore provides a bridge between the large body of research on the intergenerational transmission of inequality and the emerging research and policy literature on children's rights.