Sentiment Analysis

abhirajanchan14

hello,

For my MSc Thesis I am delving deeper in predition analysis. I know that Rapidminer has an accelerator for Sentiment Analysis, but I am wondering how they create the scores that are given as a result. If I understand well, a SVM is used on the dataset that you enter. However, the result of a SVM was to create a hyperplane that seperates points +1 from -1 right? So how are the continuous variables in the accelerator created then, and are they actually valid?

 

YYH

Hi @abhirajanchan14,

We have a template for predicting sentiments with labeled data and predictive models (basically any supervised machine learning model can be used, but linear SVM usualy outperform for term-doc table). The resulting confidence for the predicted sentiment will range from 0 to 1.

Another option is dictionary-based sentiments.

To calculate sentiment of a document we calculate sentiment of each word. Document sentiment is then calculated as the weighted average value of all word sentiments, which would generate a sentiment score range from -1 to +1.

Also check out the third party extensions: MonkeyLearn, Aylien, Rosette

 

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