4 Methods To Text Pre-Processing Using Python

In recent days, and applications of Natural language processing (NLP) has become one of the most overlooked topics lately. With so many inconsistent results from its application. For realizing that they are not following pre-processing to their texts or using the wrong ones for their project.

Introduction to text pre-processing :
Pre-processing your text means transforming your text into a predictable, analyzable form so that it can get applied to a specific task. It’s a task where you have a unique task or domain, and you have to handle the task differently. 
It is not always possible to transfer text pre-processing from one task to another. Therefore, text pre-processing cannot be directly get transferred from one task to another. Keeping all these in mind, let’s dive deeper into the various processes involved in the text pre-processing one by one.
A simple example – let’s assume that we’re trying to discover commonly used words from the dataset. If your pre-processing step is to remove stop words because of already getting used then there are chances that you may miss the common words that have already been eliminated. Therefore one process of pre-processing text doesn’t fit with the rest, so you’ve to use them separately to get the best results.  
NLTK is an advanced platform for building python programming to understand the text, pre-processing libraries for classification, tokenization, stemming, semantic reasoning, and many advanced functions. 
Stemming straightaway chops the words to their root format. So that all the derived forms of a particular word will belong to one root word. It follows porter’s algorithms, which proved to be empirically most successful for English. 
Stop words:
There is no universal list of particular stop words but you have to set your own rules and have to follow them by own. Some of the common are like – the, is, are. Stop words filters from text to be processed and does accept these words as a count. However, the NLTK module a list of stop words. 
It’s a very similar process to stemming, where the main goal is to remove the inflection of words towards their root form. The prime difference is lemmatization does it appropriately without trying any random process just by going into its root. Mapping words become very easy and systematic, and some of them are based upon rule-based approaches. 

Trouble – trouble 

Troubling – trouble 

Troubled – trouble 

Troubles – trouble

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