Raw text data is often noisy and unstructured, containing various inconsistencies such as typos, slang, abbreviations, and irrelevant information. Preprocessing helps in:

Improving Data Quality: Removing noise and irrelevant information ensures that the data fed into the model is clean and consistent.

Enhancing Model Performance: Well-preprocessed text can lead to better feature extraction, improving the performance of NLP models.

Reducing Complexity: Simplifying the text data can reduce the computational complexity and make the models more efficient.

Data is the new currency with which companies can optimize their business processes and address customers in a more targeted manner. This is why analyzing text data, for example, has a central role to play in decision-making.

NLTK (Natural Language Toolkit):

NLTK is a comprehensive library for natural language processing. It offers a variety of tools for Tokenizationstemming, lemmatization, POS tagging and more. Extensive resources such as dictionaries and corpora are also available.

spaCy:

As a modern and efficient library for natural language processing, spaCy provides pre-trained models for tasks such as tokenization, POS tagging and Named Entity Recognition (NER) ready. It is known for its speed and user-friendliness.

TextBlob:

TextBlob is based on NLTK and simplifies many of the text analysis tasks. This library is particularly user-friendly and is ideal for beginners. TextBlob offers functions such as sentiment analysis, extraction of noun phrases and more.

Tokenization refers to a process of segmenting input text into words, punctuation, etc. It allows you to identify the basic units in your text that are called tokens.

By tokenizing, you can conveniently split up text by word or by sentence. This will allow you to work with smaller pieces of text that are still relatively coherent and meaningful even outside of the context of the rest of the text. It’s your first step in turning unstructured data into structured data, which is easier to analyze.

When you’re analyzing text, you’ll be tokenizing by word and tokenizing by sentence. Here’s what both types of tokenization bring to the table:

Tokenizing by word: Words are like the atoms of natural language. They’re the smallest unit of meaning that still makes sense on its own. Tokenizing your text by word allows you to identify words that come up particularly often. 

Tokenizing by sentence: When you tokenize by sentence, you can analyze how those words relate to one another and see more context.

Sentence Detection (Sentence Boundary Detection)

Sentence Boundary Detection locates the start and end of sentences in a given text. You can divide a text into linguistically meaningful units to perform tasks such as part of speech tagging and entity extraction.

Stop words are the most common words in a language. Examples of stop words are the, who, too, and is. We usually remove the stop words because they are not significant in many text mining tasks such as word frequency analysis. You can identify and remove stop words by using NLTK's list of stop words after tokenizing the text.

Stemming refers to a text processing task that reduces words to their root. For example, the words "adventure", "adventurer", and "adventurous" share the root adventur.” Stemming allows us to reduce the complexity of the textual data so that we do not have to worry about the details of how each word was used.

Word frequency is an analysis that gives you insights into word patterns, such as common words or unique words in the text.

Part of speech (POS) analyzes the grammatical role each word plays in a sentence. In other words, it determines to which category each word (Noun, Pronoun, Adjective, Verb, Adverb, Preposition, Conjunction, and interjection) belongs. POS tags are useful when you want to assign a syntactic category to each word of the text for future analysis.

• where JJ is the tag for adjectives, NN is the tag for nouns, RB is the tag for adverbs, PRP is the tag for pronouns, and VB is the tag for verbs.

Lemmatization is the process of reducing inflected forms of a word while ensuring that the reduced form belongs to a language. This reduced form or root word is called a lemma. For example, “visits”, “visiting”, and “visited” are all forms of “visit” (lemma). Here, "visit" is the lemma. The inflection of a word also reduces numbers (car vs cars). Lemmatization is an important step because it helps you reduce the inflected forms of a word so that they can be analyzed in the text more efficiently.

Unlike tokenization, which allows you to identify every single word and sentence, chunking allows you to identify phrases in a textual input. Chunking allows you to extract a word or group of words that work as a unit to perform a grammatical function.

Chinking is often used together with chunking. While chunking is used to identify and find a pattern, chinking is used to exclude a pattern.

A named entity is an object’s assigned name, for example, a person’s name, a film, a book title, or a song’s name. NLTK can recognize these named entities in a document by asking the model for a prediction. Because the performance of the models depends on the examples they were trained on, NEF might not always work perfectly and you might need to adjust the tuning based on your case.

We'll convert the text to lowercase, remove punctuation, numbers, special characters, and HTML tags.

Expanding contractions in the text.

Converting emojis to their textual representation.

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