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What is sentiment analysis?

Sentiment analysis, also known as opinion mining, is a subfield of natural language processing (NLP) that identifies and extracts opinions.

Sentiment analysis is the interpretation and classification of positive, negative, and neutral sentiment within text data:


Sentiment Analysis Graph Sentiment Analysis Graph

In the past, companies relied on traditional methods like surveys and focus groups to gather consumer feedback. However, it is now possible to analyze text from a variety of sources with greater accuracy and less effort thanks to machine learning and artificial intelligence technologies.


Examples of sentiment analysis:

  1. Matching patients with a specialist to improve health outcomes

  2. Social media monitoring

  3. Lead quality

  4. Movie reviews

  5. Food reviews

  6. Speech reviews

  7. Brand monitoring

  8. Market research

  9. Customer feedback analysis

Why is sentiment analysis important?

Sentiment analysis helps gauge people's positive, negative, or neutral reactions to determine what people think. That information helps indicate if a product, service, or message needs to be adjusted to match an intended audience sentiment better.


Additional Resources

Tutorial: Natural Language Processing Tutorial - Sentiment Analysis

Blog post: Sentiment Analysis with VADER- Label the Unlabelled Data