Mémoire dans lequel on examine le sentiment des anciens et des modernes sur l'orgine des Macreuses.

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

Sentiment de Napoléon sur la divinité de Jésus-Christ.

At head of title: Fragments religieux inédits.

Du sentiment artistique dans la morale de Montaigne : oeuvre posthume de Edouard Reul /

Mode of access: Internet.

Songs of sentiment; a Christy gift book ...

Mode of access: Internet.

A sentiment in verse for every day in the year ...

Mode of access: Internet.

Country sentiment,

Poems reprinted in part from various periodicals.

Poems of nature and sentiment /

Added t.-p., engr.

Studies in sentiment; apple-blossom in Brittany. The eyes of pride. Countess Marie of the angels. The dying of Francis Donne.

Bound in blue paper boards. Spine lacking.

Vignettes; a miniature journal of whim and sentiment,

Mode of access: Internet.

Histoire du sentiment religieux en France au XVII siecle. Pascal et son temps par Fortunat Strowski ... 1. ptie. ...

Mode of access: Internet.

Du génie de l'architecture. Ouvrage ayant pour but de rendre cet art accessible au sentiment commun, en le rappelant a son origine, a ses propriétés, a son génie ...

Mode of access: Internet.

Anti-American sentiment and America's perceived intent to dominate: An 11-nation study

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.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

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.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.