Using first- and second-order lexical co-occurrence to assess temporal changes in media valence and content

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Canadians benefit from the social, environmental, and economic sustainability generated from natural resources. The full value of Canada's natural resource potential can best be realized when government and industry can actively work together toward a shared vision of the future that takes into account the views of stakeholders who often hold very different opinions about the best approach to natural resource development. The purpose of this proposal is to develop a method to make it easier for industry to monitor and understand the differing points of view of the stakeholders' in natural resource development. I propose to use automated (computational) approaches to extract content from media text to develop quantitative methods of characterizing public attitudes about Canadian resources. In particular, I will use methods from cognitive science which model semantic content of words by measuring the similarity of the micro-contexts in which those words appear. These models build vectors (which are just lists of numbers) for each word in the language that represent how often that word co-occurs with every other word within a small text window, that may, for example, include five words in either direction. By mathematically comparing the similarity of the vectors for any two words, it is possible to measure how similar any two words are. By combining a word's similarities to two or more words, it is possible to characterize words in terms of predefined semantic dimensions. For example, in previous work I have shown that one can look at a word's relative distance from words with a high emotional valence (i.e. words considered to be very good or very bad) to characterize that word in terms of its evaluative valence. I will use semantic co-occurrence models and other computed measures of text to characterize how different print media outlets and stakeholder groups with a vested interest in natural resources change the way they communicate about those resources across time