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An Appraisal Analysis Of The Attitudinal Resources In English Online Entertainment News

Posted on:2014-04-07Degree:MasterType:Thesis
Country:ChinaCandidate:L LiFull Text:PDF
GTID:2255330425993881Subject:Foreign Linguistics and Applied Linguistics
Abstract/Summary:PDF Full Text Request
The Appraisal Theory (AT) is a relatively new framework of lexicon and grammar which is developed in the research of interpersonal meaning from the Systemic-Functional Linguistics (SFL). Most people prefer to get information from tablet computers and smart phones in our modern age. Online entertainment news, as a recent journalism style in the information era, not only has become an increasingly important hot topic in public’s daily life but also has been receiving more and more attention in various academic fields.This study is based on the theoretical framework of The Appraisal Theory. In this thesis, the core element of the Appraisal Theory-Attitude System is applied to systematically analyze English online entertainment news. By means of quantitative and qualitative approach, the author explores the amount and distribution features of the attitudinal resources in the text of English online entertainment news as well as how these various attitudinal resources are employed to realize interpersonal meaning.From the analysis, the author draws four major findings:1.Appreciation values dominate over the other two subsystems in count;2. non-authorial Affect values are much more than authorial Affect values in the chosen corpus;3. within the category of Judgement, the amount of positive Judgement values is much larger than that of negative Judgement values. In addition, the number of propriety is the most among these five subcategories of Judgement;4.within the category of Appreciation, the amount of positive Appreciation values is much larger than that of negative Appreciation values and reaction dominates the first place in amount of all Appreciation values.
Keywords/Search Tags:Appraisal theory, attitude, online entertainment news
PDF Full Text Request
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