Sentiments on a Grid: Analysis of Streaming News and Views

Abstract

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.

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