Sentiment Analysis
Determining the emotional tone or opinion expressed in text, typically positive, negative, or neutral.
In-depth explanation
Sentiment analysis extracts subjective information from text to understand attitudes and opinions. It can be binary (positive/negative), ternary (adding neutral), or more granular. Applications include social media monitoring, customer feedback analysis, and market research. Modern approaches use pre-trained language models fine-tuned on labeled sentiment data.
Examples
Related terms
More in Natural Language Processing
Natural Language Processing (NLP)
The field of AI focused on enabling computers to understand, interpret, and generate human language.
Tokenization
Breaking text into smaller units (tokens) such as words, subwords, or characters.
Word Embedding
Dense vector representations of words that capture semantic meaning and relationships.
Named Entity Recognition (NER)
Identifying and classifying named entities in text into categories like person, organization, location.
BERT
Bidirectional Encoder Representations from Transformers, a pre-trained language model for NLP tasks.
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