$2\mathrm{.}2$ Select a small text dataset $($e$.$g$.,$ a paragraph or set of sentences$).$ Apply each of the following preprocessing techniques on this dataset:

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Tokenization: Split the text into tokens $($words$).$

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Stopwords Removal: Remove common stopwords from the tokenized text.

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Stemming: Apply a stemming algorithm $($e$.$g$.,$ Porter Stemmer$)$ to reduce words to their base forms.

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Lemmatization: Apply lemmatization to reduce words to their dictionary form. $(25$ marks$)$

$2\mathrm{.}3$ Explain each step of the preprocessing techniques applied in $2\mathrm{.}2.$ Provide insights into the significance of each technique and how it impacts the final dataset. $(15$ marks$)$