The field of automated sentiment analysis has emerged in recent years as an exciting challenge to the computational linguistics community. Research in the field investigates how emotion, bias, mood or affect is expressed in language and how this can be recognised and represented automatically. To date, the most successful applications have been in the classification of product reviews and editorials. This paper aims to open a discussion about alternative evaluation methodologies for sentiment analysis systems that broadens the scope of this new field to encompass existing work in other domains such as psychology and to exploit existing resources in diverse domains such as finance or medicine. We outline some interesting avenues for research which investigate the impact of affective text content on the human psyche and on external factors such as stock markets.

The effectiveness of CCTV surveillance networks is in part determined by their ability to perceive possible threats. Our traditional means for determining a level of threat has been to manually observe a situation through the network and take action as appropriate. The increasing scale of such surveillance networks has however made such an approach untenable, leading us look for a means by which processes may be automated. Here we investigate the language used by security experts in an attempt to look for patterns in the way in which they describe events as observed through a CCTV camera. It is suggested that natural language based descriptions of events may provide the basis for an index which may prove an important component for future automated surveillance systems.

The paper presents a corpus-based study aimed at an analysis of ontological and terminological commitments in the discourse of specialist communities. The analyzed corpus contains the lectures delivered by the Nobel Prize winners in Physics and Economics. The analysis focuses on (a) the collocational use of automatically identified domain-specific terms and (b) a description of meta-discourse in the lectures. Candidate terms are extracted based on the z-score of frequency and weirdness. Compounds comprising these candidate terms are then identified using the ontology representation system Protégé. This method is then replicated to complete analysis by including an investigation of metadiscourse markers signalling how writers project themselves into their work.

In this paper we report on constructing a finite state automaton comprising automatically extracted terminology and significant collocation patterns from a training corpus of specialist news (Reuters Financial News). The automaton can be used to unambiguously identify sentiment-bearing words that might be able to make or break people, companies, perhaps even governments. The paper presents the emerging face of corpus linguistics where a corpus is used to bootstrap both the terminology and the significant meaning bearing patterns from the corpus. Much of the current content analysis software systems require a human coder to eyeball terms and sentiment words. Such an approach might yield very good quality results on small text collections but when confronted with a 40-50 million word corpus such an approach does not scale, and a large-scale computer-based approach is required. We report on the use of Grid computing technologies and techniques to cope with this analysis.

The compilation of specialist terminology requires an understanding of how specialists coin and use terms of their specialisms. We show how an exploitation of the pragmatic features of specialist terms will help in the semi-automatic extraction of terms and in the organisation of terms in terminology data banks.