Idiom–based features in sentiment analysis: cutting the Gordian knot

In this paper we describe an automated approach to enriching sentiment analysis with idiom–based features. Specifically, we automated the development of the supporting lexico–semantic resources, which include (1) a set of rules used to identify idioms in text and (2) their sentiment polarity classifications. Our method demonstrates how idiom dictionaries, which are readily available general pedagogical resources, can be adapted into purpose–specific computational resources automatically. These resources were then used to replace the manually engineered counterparts in an existing system, which originally outperformed the baseline sentiment analysis approaches by 17 percentage points on average, taking the F–measure from 40s into 60s. The new fully automated approach outperformed the baselines by 8 percentage points on average taking the F–measure from 40s into 50s. Although the latter improvement is not as high as the one achieved with the manually engineered features, it has got the advantage of being more general in a sense that it can readily utilize an arbitrary list of idioms without the knowledge acquisition overhead previously associated with this task, thereby fully automating the original approach

Pushing the envelope of sentiment analysis beyond words and polarities

Idioms are multi-word expressions which hold a literal and figurative meaning which is conventionally understood by native speakers. Their overall meaning, often, cannot be deduced from the literal meaning of their constituent words. Sentiment analysis, also referred to as opinion mining, aims to automatically extract and classify sentiments, opinions, and emotions expressed in text. The research in this thesis is motivated by the fact that idioms, which often express an affective stance towards an entity or an event, are not featured systematically in sentiment analysis. To estimate the degree to which the inclusion of idioms as features may improve the results of traditional sentiment analysis, we compared our results to two state-of-the-art sentiment analysis approaches. Firstly, we collected a set of idioms that are relevant to sentiment analysis, i.e. those that can be mapped to an emotion. These mappings were obtained using a crowdsourcing approach. Secondly, to evaluate the results of sentiment analysis, we assembled a corpus of sentences in which idioms are used in context. Each sentence was annotated with an emotion, which formed the basis for the gold standard used for the comparison against the baseline methods. The classification performance was improved by almost 20 percentage points. Given the positive findings from our initial experiments, the main limitation was the significant knowledge-engineering overhead involved in hand-crafting lexico-semantic resources used to support idiom-based features. To minimise the bottleneck associated with the acquisition of such resources, we scaled up our original approach by automating their engineering. Subsequently, these resources were used to replace the manually engineered counterparts of such features in the originally proposed method. The fully automated approach outperformed the two baseline methods by 7 and 9 percentage points. These improvements, however, were poorer in comparison to those achieved in the initial study. Nevertheless, we have demonstrated, not only can idiom-based features be automatically engineered, but they too, improve sentiment classification results, when such features are present. Taking a long-term view of the research in this thesis, we want to address the limitations of state-of-the-art sentiment analysis approaches by focusing on a full range of emotions, rather than sentiment polarity. However, there is no consensus among researchers on a standardised framework for classifying emotions. Proposing such a framework would be a major contribution to the field of sentiment analysis, as it would stimulate its evolution into fully-fledged emotion classification and allow for systematic comparison of independent studies. With this goal in mind, we investigated the utility of different classification frameworks for sentiment analysis. A comprehensive statistical analysis of our experimental results provided explicit evidence that, in relative terms, six basic emotions are best suited for sentiment analysis. However, we identified the major shortcoming of oversimplifying positive emotions

A preliminary description of mood in Welsh

In this paper we propose a functional account of the Welsh mood system, focussing on responsives in particular. The discourse functions of responsives are interpreted through the concept of negotiation within the systemic functional linguistic framework, which offers a rich model for accounting for both initiations and responses, including possible tracking and challenging moves. By examining the interaction of mood together with specific features of Welsh, e.g. a dominant VSO clause ordering, mood particles, Subject ellipsis and a complex system of negation, we are able to show that Welsh tends to highlight interpersonal meanings in clause initial position. As the first functional description of Welsh, we also set out important directions for future research, based on the findings presented in this paper

Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems

The proliferation and application of machine learning based Intrusion Detection Systems (IDS) have allowed for more flexibility and efficiency in the automated detection of cyber attacks in Industrial Control Systems (ICS). However, the introduction of such IDSs has also created an additional attack vector; the learning models may also be subject to cyber attacks, otherwise referred to as Adversarial Machine Learning (AML). Such attacks may have severe consequences in ICS systems, as adversaries could potentially bypass the IDS. This could lead to delayed attack detection which may result in infrastructure damages, financial loss, and even loss of life. This paper explores how adversarial learning can be used to target supervised models by generating adversarial samples using the Jacobian-based Saliency Map attack and exploring classification behaviours. The analysis also includes the exploration of how such samples can support the robustness of supervised models using adversarial training. An authentic power system dataset was used to support the experiments presented herein. Overall, the classification performance of two widely used classifiers, Random Forest and J48, decreased by 16 and 20 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks.Comment: 9 pages. 7 figures. 7 tables. 46 references. Submitted to a special issue Journal of Information Security and Applications, Machine Learning Techniques for Cyber Security: Challenges and Future Trends, Elsevie

Comparing hierarchical approaches to enhance supervised emotive text classification

The performance of emotive text classification using affective hierarchical schemes (e.g. WordNet-Affect) is often evaluated using the same traditional measures used to evaluate the performance of when a finite set of isolated classes are used. However, applying such measures means the full characteristics and structure of the emotive hierarchical scheme are not considered. Thus, the overall performance of emotive text classification using emotion hierarchical schemes is often inaccurately reported and may lead to ineffective information retrieval and decision making. This paper provides a comparative investigation into how methods used in hierarchical classification problems in other domains, which extend traditional evaluation metrics to consider the characteristics of the hierarchical classification scheme can be applied and subsequently improve the classification of emotive texts. This study investigates the classification performance of three widely used classifiers, Naive Bayes, J48 Decision Tree, and SVM, following the application of the aforementioned methods. The results demonstrated that all methods improved the emotion classification. However, the most notable improvement was recorded when a depth-based method was applied to both the testing and validation data, where the precision, recall, and F1-score were significantly improved by around 70 percentage points for each classifier

A scalable and automated framework for tracking the likely adoption of emerging technologies

While new technologies are expected to revolutionise and become game-changers in improving the efficiencies and practises of our daily lives, it is also critical to investigate and understand the barriers and opportunities faced by their adopters. Such findings can serve as an additional feature in the decision-making process when analysing the risks, costs, and benefits of adopting an emerging technology in a particular setting. Although several studies have attempted to perform such investigations, these approaches adopt a qualitative data collection methodology which is limited in terms of the size of the targeted participant group and is associated with a significant manual overhead when transcribing and inferring results. This paper presents a scalable and automated framework for tracking likely adoption and/or rejection of new technologies from a large landscape of adopters. In particular, a large corpus of social media texts containing references to emerging technologies was compiled. Text mining techniques were applied to extract sentiments expressed towards technology aspects. In the context of the problem definition herein, we hypothesise that the expression of positive sentiment infers an increase in the likelihood of impacting a technology user's acceptance to adopt, integrate, and/or use the technology, and negative sentiment infers an increase in the likelihood of impacting the rejection of emerging technologies by adopters. To quantitatively test our hypothesis, a ground truth analysis was performed to validate that the sentiment captured by the text mining approach is comparable to the results given by human annotators when asked to label whether such texts positively or negatively impact their outlook towards adopting an emerging technology. The collected annotations demonstrated comparable results to those of the text mining approach, illustrating that automatically extracted sentiment expressed towards technologies are useful features in understanding the landscape faced by technology adopters, as well as serving as an important decision-making component when, for example, recognising shifts in user behaviours, new demands, and emerging uncertainties

The AHK-Wales Report Card 2018: Policy Measures - is it possible to ‘score’ qualitative data?

Comprehensive and meaningful policy analysis in the field of physical activity is difficult, not least because of the variable influence of other policy domains. However, in 2011 a Policy Assessment Tool (PAT) was developed by members of the WHO European Network for the Promotion of Health-Enhancing Physical Activity (HEPA Europe) and tested in several different countries. In 2014, Wales joined a global initiative, active healthy kids (AHK) Global Alliance, that supported the development of country level ‘Report Cards’ scoring a range of indicators that influence physical activity amongst children and young people, one of which was labelled ‘Government Strategies and Investments’. For the first two Report Cards this indicator and its associated ‘score’ was informed subjectively by expert consensus. In 2018, it was decided to utilize the Policy Audit Tool Version 2 (PAT v2) developed by HEPA Europe to aid analysis and to develop and test a scoring rubric aligned to the tool. The subsequent process indicated that the tool could be applied and translated into a ‘grade’ that could be used in conjunction with the other indicators of the AHK Report Card to generate overall Report Card grades. The use of both the HEPA PAT v2 and the scoring rubric offers an opportunity to provide greater consistency and potential for developing both comparative and trend data when assessing policy impact on physical activity in children and young people. These tools should be utilized by the AHK Global Alliance in future Report Cards

Hardening machine learning Denial of Service (DoS) defences against adversarial attacks in IoT smart home networks

Machine learning based Intrusion Detection Systems (IDS) allow flexible and efficient automated detection of cyberattacks in Internet of Things (IoT) networks. However, this has also created an additional attack vector; the machine learning models which support the IDS's decisions may also be subject to cyberattacks known as Adversarial Machine Learning (AML). In the context of IoT, AML can be used to manipulate data and network traffic that traverse through such devices. These perturbations increase the confusion in the decision boundaries of the machine learning classifier, where malicious network packets are often miss-classified as being benign. Consequently, such errors are bypassed by machine learning based detectors, which increases the potential of significantly delaying attack detection and further consequences such as personal information leakage, damaged hardware, and financial loss. Given the impact that these attacks may have, this paper proposes a rule-based approach towards generating AML attack samples and explores how they can be used to target a range of supervised machine learning classifiers used for detecting Denial of Service attacks in an IoT smart home network. The analysis explores which DoS packet features to perturb and how such adversarial samples can support increasing the robustness of supervised models using adversarial training. The results demonstrated that the performance of all the top performing classifiers were affected, decreasing a maximum of 47.2 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks

What is the evidence base for early palliative care integrated with acute oncology services in terms of oncology patient reported experience and outcomes, quality of life, and cost effectiveness? A rapid review

Many acute hospital settings include both ‘Acute Oncology Services’ (AOS) and Specialist Palliative Care (SPCT) liaison teams that contribute to the management of complex cancer patients. Acute oncology services tend to provide advice on management of cancer related issues including oncological emergencies, streamlined access to site specific oncology teams or the patient’s own oncologist, and specialist oncology services like emergency radiotherapy (National Chemo-therapy Advisory Group 2009). The role of the SPCT is broad and includes advice on management of symptoms, emotional support for patients and their families, complementary therapies, assistance with discharge planning for last days of life and for complex commu-nication. Within these roles there is sometimes crossover requiring the teams to work together closely alerting each other to patients who may benefit from the other’s specialism. There is reported evidence that meeting a specialist palliative care team early in the patient’s oncological journey can improve several outcomes including symptom severity, quality of life (Zimmermann et al. 2014) and mood (Temel et al. 2010) compared to standard oncological care (Zimmermann et al. 2014, Greer et al. 2013). The aim of this rapid review was to look at models where acute oncology and specialist palliative care teams worked together when a patient was admitted acutely to hospital to see if this combined approach improved patient outcomes. In the review itself, given the recent emergence of AOS, we didn’t find evidence for specific integration of AOS and SPCT models. There was evidence for the impact of palliative care intervention for oncology patients when admitted to acute sector