Aspect based sentiment analysis using machine learning approach I am very new in machine learning. I have annotated data with category, aspect, opinion word and sentiment. for example, for the bellow text

"The apple was really tasty"

I have category->food, aspect-> apple, opinion word ->tasty and sentiment->positive. I have training data like this format.

How can I train a SVM classifier using this format of training set?
How to extract features like n-gram, POS and sentiment word to train the classifier?
Could you please suggest any beginning step for this aspect based sentiment analysis using machine learning algorithms?
Aleksandar Blekh has given some really nice links about the big picture of how to do sentiment analysis; I'll try to provide some links to software and talk about the nitty-gritty of how to make it work. I'll point you to the example using scikit-learn (http://scikit-learn.org/stable/ ), a machine learning library in Python. 

You would first want to take your dataset and load it into scikit-learn in a sparse format. This link (http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html ) gives examples of how to load text in a bag-of-words representation, and the same module (scikit-learn.feature_extraction.text) can also count n-grams. It then describes how to run Naive Bayes and SVM on that dataset. You can take that example and start playing with it.