Computing Attitude and Affect in Text: Theory and by James G. Shanahan, Yan Qu, Janyce Wiebe

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By James G. Shanahan, Yan Qu, Janyce Wiebe

The chapters during this booklet deal with perspective, impact, and subjective opinion. quite a few conceptual types and computational tools are awarded, together with distinguishing attitudes from uncomplicated actual assertions; distinguishing among the author’s reviews from stories of alternative people’s evaluations; and distinguishing among explicitly and implicitly said attitudes. furthermore, many purposes are defined that promise to learn from the power to appreciate attitudes and have an effect on, equivalent to indexing and retrieval of files through opinion; automated query answering approximately evaluations; research of sentiment within the media and in chat groups; examining purchaser discourse in treatment and counseling; deciding on kin among clinical texts; producing extra applicable texts; and growing writers’ aids. as well as English texts, the gathering contains stories of French, jap, and Portuguese texts. The chapters are prolonged and revised models of papers offered on the American organization for synthetic Intelligence (AAAI) Spring Symposium on Exploring perspective and impact in textual content, which happened in March 2004 at Stanford college. The symposium, and the e-book which grew out it, characterize a primary foray into this sector and a stability between conceptual types, computational equipment, and functions.

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Extra info for Computing Attitude and Affect in Text: Theory and Applications (The Information Retrieval Series)

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The text in Figure 5 indicates this very phenomenon: the ranking of a certain washing machine was lowered from the year before, because of information gained about repair issues and consumer complaints, not because of different test results. The source list annotation is the product of a percolation process over the embedded structures formed by the successive attribution of the material. ”, then the basic profile encodes one level of embedding, C(Ø), where C stands for the circumstantial information provided regarding Ø.

Birnbaum, n, h, n, n)] [the proposal, which also would create a new type of individual retirement account, was fraught with budget gimmickry that would lose billions of dollars in the long run. Source-list(Democrats, Reporter Jeffrey H. Birnbaum, n, n, n, n)] Figure 7. Potential belief spaces for the text of Figure 1 for an agent with no prior knowledge or beliefs. Note that this work has important differences with traditional work on belieff reports. Utility texts such as newspaper articles expressly avoid using belief reports because they represent an evaluation by the reporter which the reader might not share.

System believes Reader believes Reporter Jeffrey H. Birnbaum believes Sen. ’’ Source-list(Sen. Packwood, h, n, n, n)] Republicans said [Republicans can garner a majority in the 100-member Senate for a capital-gains tax cut. Source-list(Republicans, n, n, n, n)] [the Democrats are unfairly using Senate rules to erect a 60-vote hurdle. Source-list(Republicans, n, h, n, n)] Democrats said [the proposal, which also would create a new type of individual retirement account, was fraught with budget gimmickry that would lose billions of dollars in the long run.

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