Is there a process flow to follow for text analytics? I am trying to draw a process flow (like a template) to be followed while on text analysis projects. So far, I've come up with this.
Text Analytics Steps

Data Collection

Acquire data
Convert data into plain text


Remove Duplicate Entries
Text Parsing and Extracting Features

Tokenization
Parsing

Remove HTML characters
Decode complex symbols to UTF-8
Spell check
Apostrophe look-up
Remove punctuation marks
Remove expressions / emojis
Split attached words
Slangs look-up
Remove URLs


Lemmatization / Stemming (Normalization of Tokens)
Parts-of-Speech Tagging


Text Filtering

Remove start-words
Remove stop-words
Remove irrelevant words based on frequency


Text Transformation

Bag of Words Representation
TF-IDF


Text Mining / Analysis (whichever analysis needed)

Text Categorization
Text Classification (supervised)
Topic Modeling (unsupervised)
Text Clustering
Similarity Analysis
Sentiment Analysis




Is this flow in the right order of steps?
What are the steps/sub-steps that I am missing?
Does the process flow look like a template or go-to flow chart when undertaking any text analytics project?
Edit: Updated process flow
This is a great place to start! While not catalogued in a "process flow", Daniel Jurafsky's book, "Speech and Language Processing" talks through the various calculations and steps related to analyzing text that you will find useful. 

The reason I say that a process flow is not provided is because Jurafsky - in great detail - explains the pros and cons of particular methods applied throughout a pipeline, and how this could change results. As an example, when calculating perplexity (an inverse metric that quantifies how well a language model can predict the next word in a statement), you should capture beginnings, ends, and stop words of statements - as opposed to other methods that require the removal of stop words.