Sentence examples for sentiment model from inspiring English sources

These results indicate that the domain-specific sentiment model produces reasonable results.

Open image in new window Fig. 1 Ortony et al.'s sentiment model.

The rest of the 80%% tweets from the domain-specific dataset were used for training the domain-specific sentiment model.

Finally, a custom sentiment model, trained on manually labeled domain-specific tweets, is applied to produce better sentiment classification results.

The framework proposed in this paper was also heavily affected by Ortony et al.'s sentiment model.

In the part of sentiment analysis, we will build a circumplex sentiment model by using hashtags as the classification tags, catching N-gram, and emoji features.

"We would like to wait until either fundamentals improve or our valuation and sentiment models give buy signals," he adds.

We also perform a comparative evaluation of different sentiment models on Egyptian and UAE tweets.

We have trained three corresponding sentiment models and compare their performance on the same testing set.

Sentiment models are built only from the positive and negative tweets.

We trained sentiment models on tweets annotated with three opinion classes downloaded from seven publicly available sentiment datasets [46].