Sentence examples for five sentiments from inspiring English sources

Not four, maybe five, sentiments.

The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods.

Experiments on five sentiment classification datasets show that ADN and IADN outperform classical semi-supervised learning algorithms, and deep learning techniques applied for sentiment classification.

In the experiment we compared performances of five sentiment analysis algorithms, namely: L general lexicon based technique that includes negation and intensification.

The algorithm splits the tokens of a sentence and calculates the sentiment by combining the tokens and constructing a tree in a bottom-up approach, where the root node is the final sentiment for the whole sentence and may contain one of the five sentiment levels: "Very Negative," "Negative," "Neutral," "Positive," and "Very Positive".

David Brooks: I hover between two sentiments.

Those three sentiments may have a contribution to make, but they are so simplistic.

Innovatively, Carpenter combined these two sentiments into something approaching a political philosophy.

The film's attempt to blur the two sentiments only highlights their disparity.

Neatly finessing those two sentiments seems to be more than Mr. Thompson's campaign can pull off.

That should discourage investors from buying shares.So far in 2012 the stockmarket seems to have been caught between these two sentiments.