Sentence examples for sentiment compared to from inspiring English sources
Control population expresses more joy emotion and positive sentiment compared to military population.
One particularly affecting tag, located beside an old olive oil factory near the town's old Armenian quarter, reads simply: "We want bread (you) thief!" It's a particularly poignant sentiment compared to all the anti-Assad rhetoric, perhaps even more so in light of growing accusations of corruption and aid-skimming within certain brigades of the Free Syrian Army.
The Dutch translation might emphasize this sentiment, compared to the original version.
More negative (76% of all weeks military expressed more negative opinions compared to the control) and less positive (30% of all weeks military expressed more positive opinions compared to the control) sentiments compared to the control population.
Nevertheless, our findings on military personnel expressing more negative emotions and sentiments compared to control population across several regions concurs with the recent report on well-being and mental-health risk among the U.S. military.
On average, over 313 weeks (from 11/2009 to 12/2014) across six Twitter points, the military population expresses: More negative (76% of all weeks military expressed more negative opinions compared to the control) and less positive (30% of all weeks military expressed more positive opinions compared to the control) sentiments compared to the control population.
This is an ironic sentiment when compared to his message in the poem "To Science" where he shows a distaste for modern science encroaching on spirituality and the artist's imagination.
It marks a sharp change in sentiment compared with reactions to previous disasters that prompted residents to open their wallets, such as a devastating earthquake that struck the Sichuan region in 2008, killing 90,000 people.
Extensive experiments to validate the perception scores Using Twitter data on several brands from diverse categories to perform experiments, the results show the relevance and effectiveness of our calculated perception as compared to sentiment analysis.
Experiments performed with open data and models built with storylines of known peer brands show the technique as highly scalable and accurate in capturing brand perception from vast amounts of social data compared to sentiment analysis.
To demonstrate the effectiveness of the devised Twitter-specific lexicon compared to a traditional sentiment lexicon, we develop comparable sentiment classification models using SVM.
