NLP Structure Question (best way for doing feature extraction)
The preprocessing pipeline depends mainly upon your problem which you are trying to solve. The use of TF-IDF, word embeddings etc. have their own restrictions and advantages.

You need to understand the problem and also the data associated with it. In order to make the best use of the data, we need to implement the proper pipeline.

Specifically for text related problems, you will find word embeddings to be very useful. TF-IDF is useful when the problem needs to be solved emphasising the words with lesser frequency. Word embeddings, on the other hand, convert the text to a N-dimensional vector which may show up similarity with some other vector. This could bring a sense of association in your data and the model can learn the best features possible.
In simple cases, we can use a bag of words representation to tokenize the texts.
So, you need to discover the best approach for your problem. If you are  solving a problems which closely resembles the famous NLP problems like IMDB review classification, sentiment analysis on Twitter data, then you can find a number of approaches on the internet.
I am building an NLP pipeline and I am trying to get my head around in regards to the optimal structure. My understanding at the moment is the following: 

Step1 - Text Pre-processing [a. Lowercasing, b. Stopwords removal, c. stemming, d. lemmatisation,] 
Step 2 - Feature extraction 
Step 3 - Classification - using the different types of classifier(linearSvC etc) 

From what I read online there are several approaches in regard to feature extraction but there isn't a solid example/answer. 

a. Is there a solid strategy for feature extraction ? 
I read online that you can do [a. Vectorising usin ScikitLearn b. TF-IDF] 
but also I read that you can use Part of Speech or word2Vec or other embedding and Name entity recognition. 
b. What is the optimal process/structure of using these? 
c. On the text pre-processing I am ding the processing on a text column on a df and the last modified version of it is what I use as an input in my classifier. If you do feature extraction do you do that in the same column or you create a new one and you only send to the classifier the features from that column? 

Thanks so much in advance