How can I deploy my features in a Machine Learning algorithm?
basically, to make things very "simple" and "shallow", all algorithm takes some sort of a numeric vector represent the features
the real work is to find how to represent the features as vector which yield the best result, this depends by the feature itself and on the algorithm using
for example to use SVM which basically find a separator plane, you need to project the features on some vectors set which yield a good enough separation, so for instance you can treat your features like this:

Emotion icons - create a vector which represent all the icons present in that tweet, define each icon to an index from 1 to n so tweet represented by [0,0,0,2,1] means the 4th and 5th icons are appearing in his body 2 and 1 times respectively
Exclamation marks - you can simply count the number of occurrences (a better approach will be to represent some more information about it like the place in a sentence and such...)
Intensity words - you can use the same approach as the Emotion icons

basically each feature can be used alone in the SVM model to classify good and bad
you can merge all those features by using 3 models of SVM and then classify by majority vote or some other method
or
you can create a long vector by concatenating all of the vectors, and feed it to the SVM

this is just a one approach, you might tweak it or use some other one to fit your data, model and goal better
I’m way new to ML so I have a really rudimentary question. I would appreciate it if one clarifies it for me.
Suppose I have a set of tweets which labeled as negative and positive. I want to perform some sentiment analysis. 
I extracted 3 basic features: 

Emotion icons 
Exclamation marks 
Intensity words(very, really etc.). 

How should I use these features with SVM or other ML algorithms? 
In other words, how should I deploy the extracted features in SVM algorithm?
I'm working with python and already know how should I run SVM or other algorithms, but I don't have any idea about the relation between extracted features and role of them in each algorithm!
Based on the responses of some experts I update my question:
At first, I wanna appreciate your time and worthy explanations. I think my problem is solving… So in line with what you said, each ML algorithm may need some vectorized features and I should find a way to represent my features as vectors. I want to explain what I got from your explanation via a rudimentary example.
Say I have emoticon icons (for example 3 icons) as one feature:
1-Hence, I should represent this feature by a vector with 3 values. 
2-The vectorized feature can initial in this way : [0,0,0] (each value represents an icon = :) and :( and :P ). 
3-Next I should go through each tweet and check whether the tweet has an icon or not. For example [2,1,0] shows that the tweet has: :) 2 times, and :( 1 time, and :p no time.
4-After I check all the tweets I will have a big vector with the size of n*3 (n is the total number of my tweets). 
5-Stages 1-4 should be done for other features.
6-Then I should merge all those features by using m models of SVM (m is the number of my features) and then classify by majority vote or some other method.
Or should create a long vector by concatenating all of the vectors, and feed it to the SVM.
Could you please correct me if there is any misunderstanding? If it is not correct I will delete it otherwise I should let it stay cause It can be practical for any beginners such as me...
Thanks a bunch…