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SVM Extract keywords used for sentiment

User: "asav_yu"
New Altair Community Member
Updated by Jocelyn
Good afternoon,

Hopefully somebody can help. I am playing around with sentiment analysis using SVM and results are very promising. My question is how can I easily extract a list of words from the document that I score to see exactly why the sentiment is negative or positive.

Example: I score a 100 word paragraph I want to see all keywords that SVM identified as being important. It would be great to have count as well, for example "bad" 4 times, "poor" 3 times.

Any advice is very much appreciated.

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    User: "B00100719"
    New Altair Community Member
    Accepted Answer
    Assuming you have used the 'Tokenize' operator, 'filter stopwords', 'transform cases', maybe also 'filter by length' experiment with and without 'stem' (probably not useful for such a small document), be sure to check "Create Word Vector" when using the 'Process Documents From Data Opeartor" which contains all these operators and also set the lower and upper pruning on that operator too - also requires experiments with different values.  The interesting words will likely be those that appear a medium number of times. 
    User: "Telcontar120"
    New Altair Community Member
    Accepted Answer
    You can also build your SVM model using the tokenized words and then use the Explain Predictions operator afterwards, which will help identify the terms that are most strongly associated with the label prediction for different groups of examples.

    User: "MartinLiebig"
    Altair Employee
    Accepted Answer
    Hi,
    in a linear svm you can also use the attribute weights which are delivered as a measure for the importance of a word for the overall descision.
    Best,
    MArtin