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.
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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?
Altair EmployeeHi,
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
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