Sentence examples for the sentiment of a document from inspiring English sources

Using CNN provides this opportunity to use n-grams to extract the sentiment of a document effectively.

As we know the order of the words in a sentence can help us to better understanding the sentiment of a document.

Several researchers such as Wang et al. [36] have considered positive, negative and neutral classes to extract the sentiment of a document (based on words and/or emoticons) and only few ones, such Khatua et al. [17], have examined the polarity degree (i.e. highly, moderately, weakly positive and negative classes).

We manually labeled the sentiment of a small subset of these documents, and translated into the appropriate language the generic sentiment lexicon used in Case Study One for implementation in this study (using the translation tool available at http://translate.google.com).com

We manually labeled the sentiment of a small subset of these documents, and also translated into Indonesian the generic sentiment lexicon used in Case Study One (in this paper all language translation was performed using the tool available at http://translate.google.com).com

"That's the sentiment of a lot of people".

In what follows we derive an algorithm for learning caug, and therefore c, by solving an optimization problem involving the labeled source and target training data, and then use c to estimate the sentiment of any new document of interest via the simple linear classifier orient = sign cTx).

More specifically, in the proposed methodology we learn caug, and therefore c, by solving an optimization problem involving the labeled and unlabeled training data, and then use c to estimate the sentiment of any new document of interest with the simple linear classifier orient = sign cTx).

Machine learning models are more popular because lexical-based approaches, which are based on the semantics of words, use a predefined list of positive and negative words to extract the sentiment of new documents.

The sentiments of a perfectionist.

Thus score estimates the sentiment or emotion of a document as a weighted average of the sentiment or emotion scores for the words comprising the document.