The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here

Sentiment Analysis

abhirajanchan14abhirajanchan14 Member Posts: 1 Learner I
edited October 2019 in Help

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?

 

Tagged:

Answers

  • yyhuangyyhuang Administrator, Employee-RapidMiner, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 364 RM Data Scientist

    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.

    sent_temp.png

    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.

    extract senti.PNG

    dic senti.PNG

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

     

Sign In or Register to comment.