Day 3: Tokenization and stopword removal

Tokenization and stop word removal are two important steps in pre-processing text data for natural language processing (NLP) tasks. These steps help to prepare the text data for further analysis, modelling, and modelling training.

Tokenization is the process of breaking down a larger piece of text into smaller units, called tokens, which can then be used for further analysis. Tokens can be words, phrases, sentences, or even stop words, depending on the NLP task. Tokenization is essential for NLP because it allows the text data to be transformed into a numerical representation that can be used as input to NLP algorithms and models.

Stop word removal is the process of removing common words, such as “the”, “and”, “a”, etc., from the text data. These words are called stop words because they do not add much meaning to the text and are not useful for NLP tasks such as sentiment analysis, text classification, and topic modelling. Stop words can also increase the size of the text data, making it more computationally expensive to process.

We will look at the example of how to perform tokenization and stop word removal using the NLTK library in Python:

It is important to note that the list of stop words is specific to each NLP task and can be customized based on the needs of the project. For example, if the NLP task involves analyzing medical text data, stop words such as “treat”, “cure”, and “medicine” may be relevant and should not be removed.


In conclusion, tokenization and stop word removal are critical steps in the pre-processing of text data for NLP tasks. Tokenization helps to transform the text data into a numerical representation that can be used as input to NLP algorithms and models, while stop word removal helps to remove irrelevant words from the text data, reducing the size of the text data and improving the efficiency of NLP algorithms and models.

Day 2: Pre-processing Text Data: Cleaning and Normalization

Day 4: Stemming and Lemmatization

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