Preprocessing is essential in NLP because raw text data is often noisy and unstructured, which can hinder the performance of machine learning models. This not only enhances the model's ability to understand the data but also improves its accuracy and efficiency in tasks like text analysis. Preprocessing ensures that the data is in a form that the model can effectively learn from and make accurate predictions. 

Lemmatization 

Lemmatization is another technique used to reduce inflected words to their root word. It describes the algorithmic process of identifying an inflected word’s “lemma” (dictionary form) based on its intended meaning. But compared to stemming, lemmatization is a slow and time-consuming process. 

As our machine understands only numerical data, we need to transform our dataset into numerical form. There are several well-known methods to convert text data into numerical format, such as:

• Bag of Words (BoW): It turns text into a fixed-length vector by counting how often each word appears, without considering grammar or word order.

• TF-IDF: Measures how important a word is in a document by comparing its frequency in that document.

• Word Embeddings: Represents words as dense vectors in a continuous vector space, capturing semantic relationships between words. 

• One-Hot Encoding: Represents words as binary vectors where each position indicates the presence or absence of a specific word in the text.