However, it is not suitable for sentiment analysis of events using microblog data since users prefer to express their feelings in individual ways, namely the same object may be expressed in several ways.
Among the feature selection methods we considered, we notice that IG and OCFS are good at distinguishing multiple classes, while CHI2, DFD, and QER are restricted to two classes, although all of them are suitable for sentiment analysis.
In addition, NBM seems to be the most suitable classifier for sentiment analysis since sentiment-expressing words tend to have low frequencies within a document, but relatively high frequencies across different documents.
Following a similar process, we also carried out experiments on English review datasets and NBM seems to be the most suitable classifier for sentiment analysis.
For the first step, this requires finding a suitable measure of sentiment as a latent factor for the valuation of asset prices.
We discover that better text features are learned for sentiment analysis when suitable weighting schemes are applied upon neural models.
The limitation of QER is that it is only suitable for classifying two classes since it is especially developed for sentiment analysis with the observation that sentiment-expressing words are usually more frequent across different reviews.
Though not overtly religious, the sentiment is morally correct and therefore Savonarola-suitable, as is the turbulent mood, so far removed from the sumptuous lyricism of Botticelli's earlier, mythological works.
What more suitable politician than a rock star in an age of manufactured sentiments and manipulated images?
