Amazon Machine Learning for sentiment analysis

Question

How flexible or supportive is the Amazon Machine Learning platform for sentiment analysis and text analytics?

Answer 1

You can build a good machine learning model for sentiment analysis using Amazon ML.

Here is a link to a github project that is doing just that: https://github.com/awslabs/machine-learning-samples/tree/master/social-media

Since the Amazon ML supports supervised learning as well as text as input attribute, you need to get a sample of data that was tagged and build the model with it.

The tagging can be based on Mechanical Turk, like in the example above, or using interns ("the summer is coming") to do that tagging for you. The benefit of having your specific tagging is that you can put your logic into the model. For example, the difference between "The beer was cold" or "The steak was cold", where one is positive and one was negative, is something that a generic system will find hard to learn.

You can also try to play with some sample data, from the project above or from this Kaggle competition for sentiment analysis on movie reviews: https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews. I used Amazon ML on that data set and got fairly good results rather easily and quickly.

Note that you can also use the Amazon ML to run real-time predictions based on the model that you are building, and you can use it to respond immediately to negative (or positive) input. See more here: http://docs.aws.amazon.com/machine-learning/latest/dg/interpreting_predictions.html