Keith Cortis

Bio: Keith Cortis is an academic researcher from Dublin City University. The author has contributed to research in topics: Sentiment analysis & Social media. The author has an hindex of 9, co-authored 23 publications receiving 253 citations. Previous affiliations of Keith Cortis include IBM & National University of Ireland, Galway.

SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

Abstract: Horizon 2020 ICT Program Project SSIX: Social Sentiment analysis financial IndeXes, has received funding from the European Union’s Horizon 2020 Research and Innovation Program ICT 2014 - Information and Communications Technologies under grant agreement No. 645425

An Ontology-Based Technique for Online Profile Resolution

Abstract: Instance matching targets the extraction, integration and matching of instances referring to the same real-world entity. In this paper we present a weighted ontology-based user profile resolution technique which targets the discovery of multiple online profiles that refer to the same person identity. The elaborate technique takes into account profile similarities at both the syntactic and semantic levels, employing text analytics on top of open data knowledge to improve its performance. A two-staged evaluation of the technique performs various experiments to determine the best out of alternative approaches. These results are then considered in an improved algorithm, which is evaluated by real users, based on their real social network data. Here, a profile matching precision rate of 0.816 is obtained. The presented Social Semantic Web technique has a number of useful applications, such as detection of untrusted known persons behind anonymous profiles, and information sharing management across multiple social networks.

MARIO Project: Validation and Evidence of Service Robots for Older People with Dementia.

Abstract: Background In the EU funded MARIO project, specific technological tools are adopted for the people living with dementia (PLWD) In the final stage of the project, a validation of the MARIO companion robot was performed from August to October 2017 Objective The aims of the present study are: 1) to illustrate the key results and evidence obtained in the final evaluation phase of the project across the three different pilot sites; 2) to assess the engagement dimensions of the PLWD who interacted with the MARIO robot; and 3) to assess the acceptability and efficacy of the MARIO companion robot on clinical, cognitive, neuropsychiatric, affective and social aspects, resilience, quality of life in PLWD, and burden level of the caregivers Methods 38 people (M = 14; F = 24) with Alzheimer's disease were screened for eligibility and all were included The following tests were administered Pre and Post interactions with MARIO: Observational Measurement of Engagement (OME), Mini-Mental State Examination (MMSE), Clock Drawing Test (CDT), Frontal Assessment Battery (FAB), Neuropsychiatric Inventory (NPI), Cornell Scale for Depression in Dementia (CSDD), Multidimensional Scale of Perceived Social Support (MSPSS), 14-item Resilience Scale (RS-14), Quality of Life in Alzheimer's Disease (QOL-AD), Caregiver Burden Inventory (CBI), Tinetti Balance Assessment (TBA), and Comprehensive Geriatric Assessment (CGA) was carried out Results In Post-MARIO interactions, significant improvements were observed in RS-14 (p = 0020)Considering the age of the people, PLWD with 68-76 years perceived that they had major social support (MSPSS Total: p = 0016) and friends to support them (MSPSS Fri: p = 0014) Indeed, the younger people (55-67 years) were less depressed (CSDD: p = 0033), and more resilient (RS-14: p = 0003) The people aged 77-85 years perceived they had major family support (MSPSS Fam: p = 0018) The participants were gender and education matched without any statistically significant difference Conclusion MARIO may be a useful tool in mitigating depression and loneliness, while enhancing social connectedness, resilience, and overall quality of life for people with dementia

A Twitter Sentiment Gold Standard for the Brexit Referendum

Abstract: In this paper, we present a sentiment-annotated Twitter gold standard for the Brexit referendum. The data set consists of 2,000 Twitter messages ("tweets") annotated with information about the sentiment expressed, the strength of the sentiment, and context dependence. This is a valuable resource for social media-based opinion mining in the context of political events.

Analysis of cyberbullying tweets in trending world events

Abstract: The use of social media amongst children, adolescents and families is nowadays a common practise in our everyday lives. Social networking sites allow social interaction between people through various channels, such as Twitter, Facebook, YouTube and blogs. Even if this interaction is generally healthy, these sites bring several risks, such as cyberbullying, depression and exposure of inappropriate content. In this paper we tackle the problem of cyberbullying via a novel approach that analyses online posts in trending world events. These generally cause a lot of interest and controversy among online Web users. Twitter is the social network of choice, where a large dataset of tweets is collected. The two current world events selected are the Ebola virus outbreak in Africa and the shooting of Michael Brown in Ferguson, Missouri. Collected tweets are carefully analysed to identify the most popular hashtags and named entities used within cyberbullying tweets. This analysis provides a basis towards several useful applications, such as a cyberbullying online post detector for certain current trending world events. This will help reduce the number of cyberbullying cases in social networking sites. Results obtained from this evaluation can be applied to other cyberbullying scenarios.

A Survey on Automatic Detection of Hate Speech in Text

Abstract: The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in many platforms and contexts, and provides a unifying definition. This area has an unquestionable potential for societal impact, particularly in online communities and digital media platforms. The development and systematization of shared resources, such as guidelines, annotated datasets in multiple languages, and algorithms, is a crucial step in advancing the automatic detection of hate speech.

The rise of artificial intelligence in healthcare applications

Abstract: Big data and machine learning are having an impact on most aspects of modern life, from entertainment, commerce, and healthcare. Netflix knows which films and series people prefer to watch, Amazon knows which items people like to buy when and where, and Google knows which symptoms and conditions people are searching for. All this data can be used for very detailed personal profiling, which may be of great value for behavioral understanding and targeting but also has potential for predicting healthcare trends. There is great optimism that the application of artificial intelligence (AI) can provide substantial improvements in all areas of healthcare from diagnostics to treatment. It is generally believed that AI tools will facilitate and enhance human work and not replace the work of physicians and other healthcare staff as such. AI is ready to support healthcare personnel with a variety of tasks from administrative workflow to clinical documentation and patient outreach as well as specialized support such as in image analysis, medical device automation, and patient monitoring. In this chapter, some of the major applications of AI in healthcare will be discussed covering both the applications that are directly associated with healthcare and those in the healthcare value chain such as drug development and ambient assisted living.