What is Text Mining?
Text mining, often referred to as text data mining or text analytics, is a process of deriving high-quality information from textual data. It involves various techniques from Natural Language Processing (NLP) and machine learning to analyze and extract meaningful patterns, trends, or insights from unstructured text sources.
Key Techniques
- Tokenization: Breaking text into individual words or phrases.
- Sentiment Analysis: Determining the sentiment or emotional tone behind a body of text.
- Named Entity Recognition (NER): Identifying entities such as names of people, organizations, or locations.
- Topic Modeling: Discovering themes or topics present in a collection of documents.
Applications
Text mining is widely used in various fields such as:
- Business: To analyze customer feedback and improve services.
- Healthcare: For extracting information from medical records.
- Social Media: To monitor public opinions and sentiment.
Conclusion
In conclusion, text mining plays a vital role in the field of Artificial Intelligence and Expert Systems by enabling systems to understand, interpret, and exploit textual data efficiently, ultimately leading to informed decision-making.