The good, the bad and the implicit: a comprehensive approach to annotating explicit and implicit sentiment

We present a fine-grained scheme for the annotation of polar sentiment in text, that accounts for explicit sentiment (so-called private states), as well as implicit expressions of sentiment (polar facts). Polar expressions are annotated below sentence level and classified according to their subjectivity status. Additionally, they are linked to one or more targets with a specific polar orientation and intensity. Other components of the annotation scheme include source attribution and the identification and classification of expressions that modify polarity. In previous research, little attention has been given to implicit sentiment, which represents a substantial amount of the polar expressions encountered in our data. An English and Dutch corpus of financial newswire, consisting of over 45,000 words each, was annotated using our scheme. A subset of this corpus was used to conduct an inter-annotator agreement study, which demonstrated that the proposed scheme can be used to reliably annotate explicit and implicit sentiment in real-world textual data, making the created corpora a useful resource for sentiment analysis

Improving customer decisions using product reviews: CROM - Car Review Opinion Miner

Online shopping is a very goal-oriented activity. Consumers have a set of preferences for a product or service that is used as criteria for assessment of the available alternatives. However, crucial information about products is often available as text reviews. Finding a product with specific features is extremely timeconsuming using the typical search functionality found in existing shopping sites. In this work we propose a method for the seamless integration of unstructured information from product reviews with structured product descriptions using opinion mining. We demonstrate our method through shopping for a used car based on 148240 car reviews. Evaluation results using a user study and simulations show that the technique enables customers to assess more product characteristics and potentially make better decisions

Staring down the lion: Uncertainty avoidance and operational risk culture in a tourism organisation

The academic literature is not clear about how uncertainty influences operational risk decision-making. This study, therefore, investigated operational risk-based decision-making in the face of uncertainty in a large African safari tourism organisation by exploring individual and perceived team member approaches to uncertainty. Convenience sampling was used to identify 15 managers across three African countries in three domains of work: safari camp; regional office; and head office. Semi-structured interviews were conducted in which vignettes were incorporated, to which participants responded with their own reactions and decisions to the situations described, as well as with ways they thought other managers would react to these specific operational contexts. The data were transcribed and qualitatively analysed through thematic coding processes. The findings indicated that approaches to uncertainty were influenced by factors including situational context, the availability and communication of information, the level of operational experience, and participants’ roles. Contextual factors alongside diverse individual emotional and cognitive influences were shown to require prudent consideration by safari tourism operators in understanding employee behavioural reactions to uncertain situations. A preliminary model drawn from the findings suggests that, in practice, decision-making in the face of uncertainty is more complex than existing theoretical studies propose. Specifically, the diverse responses anticipated by staff in response to the vignettes could guide safari tourism management towards better handling of risk under uncertainty in remote locations

Tipping the scales: exploring the added value of deep semantic processing on readability prediction and sentiment analysis

Applications which make use of natural language processing (NLP) are said to benefit more from incorporating a rich model of text meaning than from a basic representation in the form of bag-of-words. This thesis set out to explore the added value of incorporating deep semantic information in two end-user applications that normally rely mostly on superficial and lexical information, viz. readability prediction and aspect-based sentiment analysis. For both applications we apply supervised machine learning techniques and focus on the incorporation of coreference and semantic role information. To this purpose, we adapted a Dutch coreference resolution system and developed a semantic role labeler for Dutch. We tested the cross-genre robustness of both systems and in a next phase retrained them on a large corpus comprising a variety of text genres. For the readability prediction task, we first built a general-purpose corpus consisting of a large variety of text genres which was then assessed on readability. Moreover, we proposed an assessment technique which has not previously been used in readability assessment, namely crowdsourcing, and revealed that crowdsourcing is a viable alternative to the more traditional assessment technique of having experts assign labels. We built the first state-of-the-art classification-based readability prediction system relying on a rich feature space of traditional, lexical, syntactic and shallow semantic features. Furthermore, we enriched this tool by introducing new features based on coreference resolution and semantic role labeling. We then explored the added value of incorporating this deep semantic information by performing two different rounds of experiments. In the first round these features were manually in- or excluded and in the second round joint optimization experiments were performed using a wrapper-based feature selection system based on genetic algorithms. In both setups, we investigated whether there was a difference in performance when these features were derived from gold standard information compared to when they were automatically generated, which allowed us to assess the true upper bound of incorporating this type of information. Our results revealed that readability classification definitely benefits from the incorporation of semantic information in the form of coreference and semantic role features. More precisely, we found that the best results for both tasks were achieved after jointly optimizing the hyperparameters and semantic features using genetic algorithms. Contrary to our expectations, we observed that our system achieved its best performance when relying on the automatically predicted deep semantic features. This is an interesting result, as our ultimate goal is to predict readability based exclusively on automatically-derived information sources. For the aspect-based sentiment analysis task, we developed the first Dutch end-to-end system. We therefore collected a corpus of Dutch restaurant reviews and annotated each review with aspect term expressions and polarity. For the creation of our system, we distinguished three individual subtasks: aspect term extraction, aspect category classification and aspect polarity classification. We then investigated the added value of our two semantic information layers in the second subtask of aspect category classification. In a first setup, we focussed on investigating the added value of performing coreference resolution prior to classification in order to derive which implicit aspect terms (anaphors) could be linked to which explicit aspect terms (antecedents). In these experiments, we explored how the performance of a baseline classifier relying on lexical information alone would benefit from additional semantic information in the form of lexical-semantic and semantic role features. We hypothesized that if coreference resolution was performed prior to classification, more of this semantic information could be derived, i.e. for the implicit aspect terms, which would result in a better performance. In this respect, we optimized our classifier using a wrapper-based approach for feature selection and we compared a setting where we relied on gold-standard anaphor-antecedent pairs to a setting where these had been predicted. Our results revealed a very moderate performance gain and underlined that incorporating coreference information only proves useful when integrating gold-standard coreference annotations. When coreference relations were derived automatically, this led to an overall decrease in performance because of semantic mismatches. When comparing the semantic role to the lexical-semantic features, it seemed that especially the latter features allow for a better performance. In a second setup, we investigated how to resolve implicit aspect terms. We compared a setting where gold-standard coreference resolution was used for this purpose to a setting where the implicit aspects were derived from a simple subjectivity heuristic. Our results revealed that using this heuristic results in a better coverage and performance, which means that, overall, it was difficult to find an added value in resolving coreference first. Does deep semantic information help tip the scales on performance? For Dutch readability prediction, we found that it does, when integrated in a state-of-the-art classifier. By using such information for Dutch aspect-based sentiment analysis, we found that this approach adds weight to the scales, but cannot make them tip

Reliability Abstracts and Technical Reviews January-December 1968

No abstract availabl

A Lean Six Sigma maturity model for higher education institutions (HEIs)

Lean Six Sigma (LSS) is a continuous improvement methodology that aims to reduce the costs of poor quality, improve the bottom-line results and create value for both customers and shareholders. LSS has been deployed in organisations in a variety of sectors and cultures for more than two decades. However, its implementation in higher educational institutions around the world has only just begun to emerge. Furthermore, there is a lack of any empirical evidence to support any successful deployment of LSS in higher educational institutions when addressing the key challenges faced by these institutions today. Therefore, the purpose of this research is to investigate the current status of Lean Six Sigma (LSS) in UK higher educational institutions and subsequently develop a Lean Six Sigma Maturity Model which can be used to assess their current level of LSS maturity and help these institutions develop action plans and strategic objectives to successfully build maturity in LSS. The study is based on a Taguchi styled systematic literature review of papers that were published on LSS in higher education in high ranking journals in the field of LSS, academic leadership and other specialist journals, from 2000 to 2020. A descriptive survey via a questionnaire was conducted in the second phase of the data collection process and semi-structured interviews were conducted in the third phase. Based on the literature review and the findings of the empirical research, a Lean Six Sigma Maturity Model for higher educational institutions was developed and tested on a mix of UK and International higher educational institutions, along with a sample of Master Black Belts from industry. The results of the empirical study show a lack of maturity in LSS, that UK institutions are in the early stages of implementation, and that these institutions have only recently started to recognise the importance of LSS to their organisation. Therefore a maturity model for this new emerging sector is vital for its success in developing its approach to deploying LSS and will become the basis for future work and publication by the author

The implementation of knowledge management systems: An empirical study of critical success factors and a proposed model

KM is the process of creating value from the intangible assets of an enterprise. It deals with how best to leverage knowledge internally in the enterprise (in its individual employees, and the knowledge that gets built into its structures and systems) and externally to the customers and stakeholders. As KM initiatives, projects and systems are just beginning to appear in organisations, there is little research and empirical field data to guide the successful development and implementation of such systems or to guide the expectations of the potential benefits of such systems. In addition, about 84 per cent of KM programmes failed or exerted no significant impact on the adopting organisations worldwide due to inability to consider many critical factors that contribute to the success of KM project implementation. Hence, this study is an exploratory investigation into the KM implementation based on an integrated approach. This includes: (1) a comprehensive review of the relevant literature; (2) a comprehensive analysis of secondary case studies of KM implementations in 90 organisations presented in the literature, in order to arrive at the most critical factors of KM implementation and their degree of criticality; (3) exploratory global survey of 92 organisations in 23 countries that have already implemented or are in the process of implementing KM; (4) in-depth case studies of four leading organisations to understand how KM implementation processes and the critical factors identified are being addressed. Based on the empirical findings of the study, 28 critical factors were identified that must be carefully considered in the KM implementation to achieve a successful project. Moreover, the study proposes an integrated model for effective KM implementation which contains essential elements that contribute to project success.Government of Saudi Arabia represented by King Saud University