700 resultados para sentiment


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Mém. Acad. sci. Paris. 9. 331-344. 1 plate. 1780.

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At head of title: Fragments religieux inédits.

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Mode of access: Internet.

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Poems reprinted in part from various periodicals.

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Added t.-p., engr.

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Mode of access: Internet.

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Perceptions of America as a powerful but malevolent nation decrease its security. On the basis of measures derived from the stereotype content model (SCM) and image theory (IT), 5,000 college students in I I nations indicated their perceptions of the personality traits of, intentions of, and emotional reactions to the United States as well as their reactions to relevant world events (e.g., 9/11). The United States was generally perceived as competent but cold and arrogant. Although participants distinguished between the United States' government and its citizens, differences were small. Consistent with the SCM and IT, viewing the United States as intent on domination predicted perceptions of lack of warmth and of arrogance but not of competence and status. The discussion addresses implications for terrorist recruitment and ally support.

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Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.