Publications

1-”EMOTIVE Ontology: Extracting fine-grained emotions from terse, informal messages”

Abstract:

With the uptake of social media, such as Facebook and Twitter, there is now a vast amount of new user generated content on a daily basis, much of it in the form of short, informal free-form text. Businesses, institutions, governments and law enforcement organisations are now actively seeking ways to monitor and more generally analyse public response to various events, products and services. Our primary aim in this project was the development of an approach for capturing a wide and comprehensive range of emotions from sparse, text based messages in social-media, such as Twitter, to help monitor emotional responses to events. Prior work has focused mostly on negative / positive sentiment classification tasks, and although numerous approaches employ highly elaborate and effective techniques with some success, the sentiment or emotion granularity is generally limiting and arguably not always most appropriate for real-world problems.

In this paper we employ an ontology engineering approach to the problem of fine-grained emotion detection in sparse messages. Messages are also processed using a custom NLP pipeline, which is appropriate for the sparse and informal nature of text encountered on micro-blogs. Our approach detects a range of eight high-level emotions; anger, confusion, disgust, fear, happiness, sadness, shame and surprise. We report f-measures (recall and precision) and compare our approach to two related approaches from recent literature.

Keywords:

Sparse Text Analysis, Ontologies, Sentiment Analysis, Emotion Analysis, Information Retrieval, Twitter.

Suggested Reference:

Sykora M., T. W. Jackson, A. O’Brien and S. Elayan, 2013. EMOTIVE Ontology: Extracting fine-grained emotions from terse, informal messages, IADIS Intelligent Systems and Agents Conference, Prague (Czech Republic)

2-”National security and social media monitoring: A presentation of the EMOTIVE and related systems”

Abstract:

Today social media streams, such as Twitter, represent vast amounts of ‘real-time’ daily streaming data. Topics on these streams cover every range of human communication, ranging from banal banter, to serious reactions to events and information sharing regarding any imaginable product, item or entity. It has now become the norm for publicly visible events to break news over social media streams first, and only then followed by main stream media picking up on the news. It has been suggested in literature that social-media are a valid, valuable and effective real-time tool for gauging public subjective reactions to events and entities. Due to the vast big-data that is generated on a daily basis on social media streams, monitoring and gauging public reactions has to be automated and most of all scalable – i.e. human, expert monitoring is generally unfeasible. In this paper the EMOTIVE system, a project funded jointly by the DSTL (Defence Science and Technology Laboratory) and EPSRC, which focuses on monitoring fine-grained emotional responses relating to events of national security importance, will be presented. Similar systems for monitoring national security events are also presented and the primary traits of such national security social media monitoring systems are introduced and discussed.

Keywords:

social media monitoring, national security, information retrieval, natural language processing, Twitter.

Suggested Reference:

Sykora M., T. W. Jackson, A. O’Brien and S. Elayan, 2013. National security and social media monitoring: A presentation of the EMOTIVE and related systems, IEEE European Intelligence and Security Informatics Conference, Uppsala (Sweden)