Sentence examples for developed a sentiment from inspiring English sources

Medikly developed a sentiment engine (or a preference discovery engine) so that marketers can search for a keyword like "diabetes" and get visibility into what doctors are talking about, what types of conversations they're having about a particular drug or pharma company and so on.

They propose a non-personal, evolutionary sort of rationality: individuals who develop a sentiment of identification and sense of belonging end up better off in the evolutionary race; hence we have inherited such propensities.

CEO Chris DeVore says that while the service could develop a sentiment engine to ensure that apps being linked to in tweets are actually being shared with a positive comment, it's actually very rare for people to tweet a link to an app they don't like, so they don't do this processing.

Arias et al. (2014) developed a public sentiment indicator from Twitter messages and investigated two domains – stock market and movie box office revenue using two forecasting models.

We developed a lexicon-based sentiment analysis algorithm that differs from existing models in the way that it aggregates the sentiment values of positive and negative words within a message.

President Obama and his successors will need to develop a "public sentiment" that it is time to test the value and limits of diplomacy to resolve some of America's most serious national-security challenges.

The outcry over Mr Berlusconi's possible return, they think, demonstrates that Europeans are at last developing an elusive sentiment: a common political consciousness that might serve as the basis for further integration.

We developed a sustainable strategy for corporate governance based on the sentiment analysis of Chinese financial reports with CSR.

Adam Smith developed a comprehensive and somewhat unusual version of moral sentimentalism in his Theory of Moral Sentiments (1759, TMS).

We developed a regression model for the CSR score and financial report sentiment, shown below: Logleft {CSR}_{it}right)=alpha.

In this research, we introduce an approach to supervised feature reduction using n-grams and statistical analysis to develop a Twitter-specific lexicon for sentiment analysis.