Text analytics techniques can be categorized into several types, each serving different purposes in extracting insights from unstructured text data. Here are the main types with explanations and examples:
Text Classification:
This technique assigns predefined categories or labels to text documents. It is widely used for sentiment analysis, spam detection, and topic categorization.
Example: Classifying customer reviews as "positive," "negative," or "neutral."
Sentiment Analysis:
Also known as opinion mining, this technique identifies and extracts subjective information to determine the sentiment expressed in text, such as emotions, opinions, or attitudes.
Example: Analyzing social media posts to gauge public sentiment about a new product launch.
Named Entity Recognition (NER):
NER identifies and classifies entities in text into predefined categories such as names of people, organizations, locations, dates, and more.
Example: Extracting company names and locations from news articles for market research.
Topic Modeling:
This technique uncovers hidden thematic structures in a large collection of documents, helping to identify topics discussed.
Example: Discovering common themes in customer feedback, such as "delivery issues" or "product quality."
Text Summarization:
This technique generates concise summaries of long documents while retaining the key information. It can be extractive (selecting important sentences) or abstractive (generating new sentences).
Example: Creating a summary of a lengthy research paper for quick review.
Information Extraction:
This technique identifies and extracts structured information from unstructured text, such as relationships between entities or specific data points.
Example: Extracting product details like price, brand, and specifications from e-commerce product descriptions.
Text Clustering:
This technique groups similar documents together based on their content, without predefined labels. It is useful for organizing large datasets.
Example: Grouping customer support tickets into clusters based on similar issues for efficient resolution.
Language Detection:
This technique identifies the language of a given text, which is useful for multilingual applications.
Example: Detecting the language of user-generated content on a global platform to route it to the appropriate processing pipeline.
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