Titlebook: Computing Attitude and Affect in Text: Theory and Applications; James G. Shanahan,Yan Qu,Janyce Wiebe Book 2006 Springer Science+Business

CORE

Validating the Coverage of Lexical Resources for Affect Analysis and Automatically Classifying New tion in computational linguistics until recently. However, now that messages (including spam) have become more prevalent than edited texts (such as newswire), recognizing this emotive dimension of written text is becoming more important. One resource needed for identifying affect in text is a lexico

locus-ceruleus

A Computational Semantic Lexicon of French Verbs of Emotion,ng this lexicon to provide an interpretation and to generate paraphrases. Semantic representations are described by means of a set of feature structures. Sixty newspaper “l(fā)etters to the Editor” were taken as a domain for the evaluation of this work.

侵略者

Extracting Opinion Propositions and Opinion Holders using Syntactic and Lexical Cues, or “claim” that express opinions, and the holders of these opinions. An extension of semantic parsing techniques is proposed that, coupled with additional lexical and syntactic features, can extract these propositional opinions and their opinion holders. A small corpus of 5,139 sentences is annotat

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Approaches for Automatically Tagging Affect: Steps Toward an Effective and Efficient Tool,s chapter describes an evaluation of a range of different tagging techniques to automatically determine the attitude of speakers in transcribed psychiatric dialogues. It presents results in a marriage-counseling domain that classifies the attitude and emotional commitment of the participants to a pa

軟膏

Argumentative Zoning for Improved Citation Indexing,tation might fit into the overall argumentation of the article: as part of the solution, as rival approach or as flawed approach that justifies the current research. Our motivation for this work is to improve citation indexing. The method we use for this task is machine learning from indicators of a

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哭得清醒了

Generating More-Positive and More-Negative Text, a set of attitudinal nuances, with particular focus on its near-synonyms. Then we use our text generator to produce a text with the same meaning but changed semantic orientation (more positive or more negative) by replacing, wherever possible, words with near-synonyms that differ in their expressed

偽證

Identifying Interpersonal Distance using Systemic Features, determination system and the role it plays in constructing interpersonal distance. By using a hierarchical system model that represents the author’s language choices, it is possible to construct a richer and more informative feature representation with superior computational efficiency than the usu

王得到

Corpus-Based Study of Scientific Methodology: Comparing the Historical and Experimental Sciences,ts that represent different types of conjunctions and modal assessment, which together can partially indicate how different genres structure text and may prefer certain classes of attitudes towards propositions in the text. This enables analysis of large-scale rhetorical differences between genres b

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