Recently my research and development work within the EMOTIVE project - looking at fine-grained, cross-cultural emotion detection from Twitter datasets - has received considerable national and international mass-media attention. Despite its' relatively low budget the project was an overall success; achieving the highest currently known performance on test-datasets in the world (in terms of F-Measure) and processing tweets at a speed of 1500-2000 tweets per second (on an avg. dual-core processor). The system that was developed detects a range of 8 "Basic Emotions", anger, disgust, fear, happiness, sadness, surprise, and shame and confusion, rather than a variation on the less informative positive / negative sentiment score.
The ontology employed gives a rich linguistic context to the eight emotions through its ability to analyse both ordinary speech and slang. This ability to monitor how the public mood changes over time is particularly useful when assessing what interventions are most successful in dealing with civil unrest or concern. However, potential analysis of tweets with the developed system can range from marketing to personality profiling through computational models which are based on emotions.
To me the project was particularly interesting mainly as it allowed me to further focus on my interests that I had throughout my PhD and also to further delve into very interesting social-media questions, NLP related issues in sparse texts processing (i.e. 140 characters per message vs. traditional NLP on large documents) and Ontology processing applications.
The work has resulted in several conference papers (and at least one Journal paper is on the way, with more in the pipeline). Currently we are continuing work within another EPSRC & DSTL funded project and are in collaboration with several organisations to further explore applications of our fine-grained emotions detection system.
