Tareas clave en PLN:

• 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.

En resumen, el PLN es una tecnología clave que permite a las máquinas procesar y comprender el lenguaje humano. Al dominar los conceptos básicos del PLN, como el preprocesamiento de texto, la clasificación y los embeddings, se sientan las bases para tareas más avanzadas como el análisis de sentimientos y más allá.