Evaluation of verbal user ratings
Hello everybody!
I started experimenting with Rapid Miner a few days ago and now have a question. As data input, I use an Excel file with 100 written reviews for an app. I have already managed to process the data and arrange it in CLuster. Now I want to go a step further and expand the detailing.
As an an example for the input data:
User A: "I like the app very well"
User B: "The app does not work, no connection"
User C: "The app crashes."
...
Now I would like the data to be roughly represented as follows:
User A: like app
User B: Does not work, no connection
User C: app crashes
...
It should therefore be searched for common terms and then each be assigned as the keyword of the corresponding rating. Can you tell me if such a thing is possible and if so how can I do this best?
Many thanks.
Best Answer
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Hi,
what you search for is called Sentiment Analysis. You can do this in a few ways. E.g. Dictionary Based, with a supervised learning method or an extension like AYLIEN. Just search for it on the forums. There is plenty of material.
Cheers,
MArtin
1
Altair EmployeeAnswers
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Hi,
what you search for is called Sentiment Analysis. You can do this in a few ways. E.g. Dictionary Based, with a supervised learning method or an extension like AYLIEN. Just search for it on the forums. There is plenty of material.
Cheers,
MArtin
1 -
I have now managed to analyze the sentiment of the reviews using AYLIEN. Now I would like to identify the key aspects of each rating. I've already noticed the "Analyze Aspect-Based Sentiment" operator, but unfortunately it doesn't offer me the desired domain. Which is the best way to solve this?
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