I have received tens of thousands of user reviews on the app.
I know the meaning of many of the comments are the same.
I can not read all these comments. Therefore, I would like to use a python program to analyze all comments, Identify the most frequently the most important feedback information.
I would like to ask, how can I do that?
I can download an app all comments, also a preliminary understanding of the Google Prediction API.
You can use the Google Prediction API to characterize your comments as important or unimportant. What you'd want to do is manually classify a subset of your comments. Then you upload the manually classified model to Google Cloud Storage and, using the Prediction API, train your model. This step is asynchronous and can take some time. Once the trained model is ready, you can use it to programmatically classify the remaining (and any future) comments.
Note that the more comments you classify manually (i.e. the larger your training set), the more accurate your programmatic classifications will be. Also, you can extend this idea as follows: instead of a binary classification (important/unimportant), you could use grades of importance, e.g. on a 1-5 scale. Of course, that entails more manual labor in constructing your model so the best strategy will be a function of your needs and how much time you can spend building the model.
