Sentence examples for sentiment values from inspiring English sources

Lists of positive and negative words and short phrases were given predefined sentiment values, and text was then given an overall score according to which ones it contained.

The numbers in brackets represent the sentiment values taken from the sentiment lexicon.

In the experiment, we selected 50 micro-blog users and found their sentiment values in 10 periods.

Finally, sentiment values from users in different time periods were selected as original data matrix, using the fuzzy clustering algorithm.

Each row in above matrix represents a QQ zone users; each column represents sentiment values in a certain period.

Then, the messages were reprocessed and computed by sentiment algorithm; we can obtaine the sentiment values of the messages in different periods.

Each of these areas was individually checked carefully against maps of New York geography to characterize the reason for the sentiment value.

Rather than reversing the sentiment value we proposed to formulate a negating function that calculates the sentiment value of a negated word.

In this module, the sentiment value for each item's feature is computed.

General Sentiment's technology evaluates the volume of mentions and sentiment value regarding a brand, company or person.

Next, combined with sentiment words and influence of modifiers on sentiment intensity, sentiment value of the whole text is calculated.