Natural Language Processing Fundamentals

Text Processing

Natural Language Processing (NLP)

• Study of how computers process, understand, and generate human language
• Involves machine learning, deep learning, and linguistics
• Applications: language translation, sentiment analysis, text summarization, chatbots

Named Entity Recognition (NER)

• Identifying named entities in unstructured text (e.g. names, locations, organizations)
• Techniques: rule-based, machine learning, hybrid approaches
• Applications: information extraction, text mining, sentiment analysis

Text Classification

• Assigning predefined categories or labels to text data (e.g. spam/not spam, positive/negative review)
• Techniques: supervised learning, naive bayes, decision trees, support vector machines
• Applications: sentiment analysis, email filtering, topic modeling

Tokenization

• Breaking down text into individual words or tokens
• Techniques: whitespace tokenization, punctuation-based tokenization, wordpiece tokenization
• Applications: text preprocessing, language modeling, sentiment analysis

Sentiment Analysis

• Determining the emotional tone or sentiment of text data (e.g. positive, negative, neutral)
• Techniques: machine learning, rule-based approaches, lexicon-based approaches
• Applications: customer feedback analysis, opinion mining, reputation management

BASICO

• Not a widely recognized term in the context of text processing
• Possible meaning: Basic Artificial Intelligence for Sentiment and Opinion
• Note: BASICO is not a standard term in the field of text processing, and its meaning may vary depending on the context.

Test your knowledge of natural language processing concepts, including text classification, sentiment analysis, tokenization, and named entity recognition. Learn how machines understand and generate human language.