The MISLEM Project : developing meta-level quality indicators for establishing a systematic linkage between educational institutions and the labour market:education, employment and graduate employability: project manual

The MISLEM Project comprised representatives from Higher and Vocational Education in four partner countries, Austria, Romania, Slovenia and the UK. In addition to this, representatives from a major UK graduate employment agency and the Austria Quality Assurance Agency were also involved. At the inaugural meeting of the Project, partner teams discussed and agreed upon appropriate methodological processes with which to carry the Project forward.

Emergent strategy and the measurement of performance:the formulation of performance indicators at the microlevel

This article presents some evidence on an aspect of the design of a strategic control system, at the microlevel, within a single organization. The research we report used an ethnographic approach to provide an understanding of strategy formulation. Our aim is to contribute to an area of literature which is of increasing significance, but relatively underdeveloped in terms of the application of in-depth, field-research techniques. We take an intensive look at the manner in which performance measures are formulated, at the microlevel, within a single organization. The article presents, as an in-depth case analysis, the experience of a fisheries holding company in New Zealand. The article recounts the experiences of managers within the organization of the process of identification of such things as critical success factors and key performance indicators (KPIs) and, more broadly, the formulation of a strategic performance measurement system.

Sustainability practices and indicators in food retail logistics:findings from an exploratory study

The aim of this paper is to provide an overview and an analysis of recent developments and changes in the implementation of sustainability practices by food retailers. It also aims to explore whether the sustainability measurement criteria and indicators identified in the literature can be applied in practice. A literature review identified the current trends, developments and the proposed sustainability objectives, criteria and indicators. Via case study research, we collected empirical data from four retailers. This involved both qualitative and quantitative data drawn from questionnaires and in-depth interviews with logistics directors from four retailers' distribution centres. The empirical data collected from the interviews indicate similarities in some of the characteristics of distribution centres, as well as differences. However, it was difficult to make cross-company comparisons due to the absence of benchmarks or assessments of the relative importance of each sustainability criterion and indicator. This research focused only on two sustainability objectives. Further research on other sustainability objectives is therefore required. Lessons learnt from the four case studies can be taken into consideration when developing future sustainability performance rating scales. The paper provides an in-depth analysis of sustainability in the food chain, with emphasis on food retailing. Its value lies in presenting an attempt to test in practice how a number of sustainability objectives, criteria and indicators are applied in logistics-related processes, identifying the gaps and reporting the potential difficulties.

The selection of biodiversity indicators for business landholdings

Businesses are seen as the next stage in delivering biodiversity improvements linked to local and UK Biodiversity Action Plans. Global discussion of biodiversity continues to grow, with the Millennium Ecosystem Assessment, updates to the Convention on Biological Diversity and The Economics of Ecosystems and Biodiversity being published during the time of this project. These publications and others detail the importance of biodiversity protection and also the lack of strategies to deliver this at an operational level. Pressure on UK landholding businesses is combined with significant business opportunities associated with biodiversity engagement. However, the measurement and reporting of biodiversity by business is currently limited by the complexity of the term and the lack of suitable procedures for the selection of metrics. Literature reviews identified confusion surrounding biodiversity as a term, limited academic literature regarding business and choice of biodiversity indicators. The aim of the project was to develop a methodology to enable companies to identify, quantify and monitor biodiversity. Research case studies interviews were undertaken with 10 collaborating organisations, selected to represent =best practice‘ examples and various situations. Information gained through case studies was combined with that from existing literature. This was used to develop a methodology for the selection of biodiversity indicators for company landholdings. The indicator selection methodology was discussed during a second stage of case study interviews with 4 collaborating companies. The information and opinions gained during this research was used to modify the methodology and provide the final biodiversity indicator selection methodology. The methodology was then tested through implementation at a mineral extraction site operated by a multi-national aggregates company. It was found that the methodology was a suitable process for implementation of global and national systems and conceptual frameworks at the practitioner scale. Further testing of robustness by independent parties is recommended to improve the system.

Geospatial data quality indicators

Indicators which summarise the characteristics of spatiotemporal data coverages significantly simplify quality evaluation, decision making and justification processes by providing a number of quality cues that are easy to manage and avoiding information overflow. Criteria which are commonly prioritised in evaluating spatial data quality and assessing a dataset’s fitness for use include lineage, completeness, logical consistency, positional accuracy, temporal and attribute accuracy. However, user requirements may go far beyond these broadlyaccepted spatial quality metrics, to incorporate specific and complex factors which are less easily measured. This paper discusses the results of a study of high level user requirements in geospatial data selection and data quality evaluation. It reports on the geospatial data quality indicators which were identified as user priorities, and which can potentially be standardised to enable intercomparison of datasets against user requirements. We briefly describe the implications for tools and standards to support the communication and intercomparison of data quality, and the ways in which these can contribute to the generation of a GEO label.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Self-training from labeled features for sentiment analysis

Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents.

Incorporating sentiment prior knowledge for weakly supervised sentiment analysis

This article presents two novel approaches for incorporating sentiment prior knowledge into the topic model for weakly supervised sentiment analysis where sentiment labels are considered as topics. One is by modifying the Dirichlet prior for topic-word distribution (LDA-DP), the other is by augmenting the model objective function through adding terms that express preferences on expectations of sentiment labels of the lexicon words using generalized expectation criteria (LDA-GE). We conducted extensive experiments on English movie review data and multi-domain sentiment dataset as well as Chinese product reviews about mobile phones, digital cameras, MP3 players, and monitors. The results show that while both LDA-DP and LDAGE perform comparably to existing weakly supervised sentiment classification algorithms, they are much simpler and computationally efficient, rendering themmore suitable for online and real-time sentiment classification on the Web. We observed that LDA-GE is more effective than LDA-DP, suggesting that it should be preferred when considering employing the topic model for sentiment analysis. Moreover, both models are able to extract highly domain-salient polarity words from text.