Sentence examples similar to Textual sentiment classifiers from inspiring English sources

The Naive Bayes algorithm was applied to classify textual sentiment, which was used as the basis for constructing the indicator for the degree of divergence.

The Naive Bayes algorithm is currently one of the most popular training algorithms for classifying textual sentiment (Antweiler and Frank 2004; Leung and Ton 2015; Sabherwal et al. 2011).

It is worth noticing that CCR and transfer learning are used in textual sentiment analysis for the first time.

However, most approaches in the field incorporated feature engineering to build efficient sentiment classifiers.

In the experiments conducted based on Amazon.com data, we find that, first, textual sentiment analysis produces a better summary of customer opinions than rating scores.

The third method-machine learning-uses computer algorithms for the classification of textual sentiment.

In addition, high overall accuracy in classification of textual sentiment has been achieved through the machine learning method.

For research on English forums, machine learning or tagging of posts is used to determine textual sentiment.

It is shown in this paper that this procedure enables accurate sentiment classifiers to be learned.

The architecture of proposed model using four sentiment classifiers is disposed in "Proposed methodology for optimization of sentiment prediction using weka" section.

The refined training samples are used to build up an effective cross-lingual sentiment classifier focusing on the target language.