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

使用一阶和二阶词汇共现来评估媒体效价和内容的时间变化

• 批准号: 484881-2015
• 负责人: Westbury, Chris
• 金额: $ 1.82万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=584600
• 关键词: Using; first; second; order; lexical

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

加拿大人从自然产生的社会，环境和经济可持续性中受益 资源。当政府和 行业可以积极地朝着对未来的共同构想，以考虑到未来的观点 对最佳自然资源开发方法的看法通常非常不同的利益相关者。 该建议的目的是开发一种方法，使行业更容易监视和理解 利益相关者在自然资源开发中的不同观点。我建议使用自动化 （计算）从媒体文本中提取内容以开发定量方法的方法 表征公众对加拿大资源的态度。特别是，我将使用认知的方法 科学通过测量微观上下文的相似性来模拟单词的语义内容 这些话出现。这些模型在语言中为每个单词构建向量（仅是数字列表） 这表示该单词与一个小文本窗口中的每个其他单词共同出现的频率，这可能是 例如，在任一方向上包括五个单词。通过数学上比较向量的相似性 任何两个词，都可以衡量任何两个词的相似之处。通过将单词的相似性与 两个或多个单词，可以根据预定义的语义维度来表征单词。为了 例如，在以前的工作中，我已经表明，可以看一个单词的相对距离与高的单词 情感价（即被认为是非常好或非常坏的单词）以此来表征该词 评估价。我将使用语义同时出现模型和其他计算的文本度量 表征对自然资源既得利益的不同印刷媒体和利益相关者群体的不同 改变他们跨时间关于这些资源的交流方式