Crowdsourcing Argumentation Structures in Chinese Hotel Reviews

Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique to collect argumentation annotations in Chinese hotel reviews. As the first Chinese argumentation dataset, our corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.Comment: 6 pages,3 figures,This article has been submitted to "The 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC2017)

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

A literature survey of methods for analysis of subjective language

Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area

Critical success factors for preventing E-banking fraud

E-Banking fraud is an issue being experienced globally and is continuing to prove costly to both banks and customers. Frauds in e-banking services occur as a result of various compromises in security ranging from weak authentication systems to insufficient internal controls. Lack of research in this area is problematic for practitioners so there is need to conduct research to help improve security and prevent stakeholders from losing confidence in the system. The purpose of this paper is to understand factors that could be critical in strengthening fraud prevention systems in electronic banking. The paper reviews relevant literatures to help identify potential critical success factors of frauds prevention in e-banking. Our findings show that beyond technology, there are other factors that need to be considered such as internal controls, customer education and staff education etc. These findings will help assist banks and regulators with information on specific areas that should be addressed to build on their existing fraud prevention systems

Toolkit on tackling error, fraud and corruption in social protection programs

This toolkit is intended to provide a concise but thorough resource for social protection practitioners on how to minimize error, fraud and corruption in their program(s). It is organized in five sections. The first section introduces the topic, by clarifying the key concepts and spelling out the rationale for this activity. The second section provides a generic framework for combating error, fraud, and corruption (EFC), developed around four building blocks of prevention; detection; deterrence; and measurement. The third section reviews the instruments, tools and mechanisms used to combat EFC in social protection programs, structured by strategic actions (prevention; detection; deterrence), by level of government and over time (how these tools and instruments can be developed over time). Section four includes generic terms of reference for the assessment of the mechanisms for combating EFC in social protection programs. The last section summarizes the main findings for three such diagnostics from the Kyrgyz Republic and the Ukraine.Public Sector Corruption&Anticorruption Measures,Insurance&Risk Mitigation,E-Business,Debt Markets,Emerging Markets

'We're not like that': Crusader and Maverick Occupational Identity Resistance

This article explores the occupational identities of hairdressers and vehicle mechanics working in small and micro-firms. Using qualitative interview data from two UK cities, it examines the ways that workers expounded, reflected on and discursively reframed public perceptions of their occupation. A novel distinction between two types of identity reframing is proposed. ‘Crusaders’ are workers who reject characterisations as inappropriate for the occupation at large, whereas ‘mavericks’ accept that popular characterisations apply to other workers but differentiate themselves. The analysis identifies differences in occupational identity resistance strategies (crusader or maverick) when workers interact with two different publics: customers and trainees