Sentence examples for prediction sentiment from inspiring English sources

To help social networks analysis, many machine learning (ML) algorithms have been adopted, e.g. user classification, link prediction, sentiment analysis, recommendations, etc.

We believe that using Deep Learning to predict sentiment of authors can help us to overcome these problems and increase the accuracy of prediction.

For short-term market prediction, other sentiment measures — like the level of bullishness among investment advisory newsletters, the put-call ratio for equity options and surveys of the views of individual investors — have a better track record.

While in some cases that might be more of a wish than an informed prediction, the sentiment reflects the considerable obstacles that Mr. Gore faces this time around, and the price he would pay, in terms of his political viability and personal legacy, should he fail again.

We plot true %ILI (ILI), predictions from sentiment and emotion features made using with AdaBoost (ABR), Linear (LR) and RandomForest (RFR) regressors.

Existing approaches of sentiment prediction and optimization widely includes SVM and Naïve Bayes classifiers.

A very few researchers have used J48, BFTree and OneR for the task of sentiment prediction.

The working methodology of proposed work for optimization of sentiment prediction is given below in Fig. 2.

Naturally, part of the bias is observed in sentiment prediction, an intrinsic property of some methods due to the way they are designed.

The sentiment prediction methods using recursive neural networks and deep convolution neural networks are bit complex in capturing compositionality of words.

The demonstration of OneR algorithm for sentiment prediction with smallest error of classification is given below: Step 1:  Select a featured term from training set.