Summary  
This chapter covers techniques for transforming raw text into machine-readable numerical data—such as tokenization, stop-word removal, vocabulary construction, and encoding—for use in downstream models.  

General domain of usage  
Sentiment analysis

**NLP** enables machines to read, understand, and generate human language. By applying various algorithms and models, NLP systems can perform tasks such as speech recognition, translation, summarization, and sentiment analysis.

Definition

**Key tasks in NLP**:  
- **Text preprocessing**: involves cleaning the text data to make it suitable for analysis. Common preprocessing steps include tokenization, removing stop words, and stemming or lemmatization;
- **Text classification**: assigning categories or labels to text data. Sentiment analysis is one example, where the goal is to classify text as positive, negative, or neutral;
- **Named entity recognition (NER)**: identifying and classifying entities in text, such as names of people, organizations, locations, and dates;
- **Part-of-speech tagging**: determining the grammatical structure of a sentence by identifying parts of speech like nouns, verbs, adjectives, etc.;
- **Sentiment analysis**: the primary task of this section. Sentiment analysis involves determining the sentiment or emotion expressed in a piece of text. This is commonly used in analyzing social media posts, customer reviews, and feedback, and is typically performed using machine learning models trained on labeled data.

- The process of splitting text into individual words or phrases, known as **tokens**;
- Tokenization is a fundamental step in NLP for turning raw text into structured data.

Tokenization

- Removing commonly used words (e.g., "the", "is", "and") that do not contribute much meaning to the analysis;
- Stop words are usually removed to reduce noise and focus on more meaningful terms in the text.

Stop words removal

- Reducing words to their base or root form, e.g., "running" becomes "run";
- Lemmatization is generally more accurate than stemming as it considers the context of words;
- Both techniques help standardize words for better analysis and comparison.

Stemming and lemmatization

- Representing words in numerical form using vectors;
- Common techniques like Word2Vec and GloVe help capture semantic relationships between words;
- Word embeddings make it possible to analyze words based on their meanings and relationships.

Word embeddings

In summary, NLP is a key technology enabling machines to process and understand human language. By mastering the basics of NLP, such as text preprocessing, classification, and embeddings, you lay the foundation for more advanced tasks like sentiment analysis and beyond.


Which of the following is a key task in NLP?

Master Recurrent neural networks and their advanced variants like LSTMs and GRUs using PyTorch. Gain hands-on experience processing sequential data for practical applications. Apply these powerful models to tackle real-world challenges in time series forecasting and various Natural language processing tasks.

Covers the limitations of traditional neural networks for sequential data and introduces the fundamentals of Recurrent Neural Networks. Explains RNN architecture, types, and step-by-step implementation through basic examples and a coding challenge.

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NLP Basics