Semantic
Language
Key Textual Metrics
Feature Extraction
Text Clustering
Key Techniques for Text Data Mining
Information Retrieval
Text Analysis Techniques
Semantic Analysis 
Semantic analysis is a subfield of business analytics that focuses on understanding the meaning and context of textual data
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Language (K)
Language has a vocabulary that contains
semantic information and a grammar that relates words to one another
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Key Textual Metrics can be broadly categorized into several types: Descriptive Metrics Sentiment Metrics Structural Metrics
Semantic Metrics Comparative Metrics 1
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Feature Extraction Word Embeddings A technique that represents words in a continuous vector space, capturing
semantic relationships between words
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Text Clustering Clustering Hierarchical Clustering DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Latent
Semantic Analysis Spectral Clustering K-means Clustering K-means is one of the most popular clustering algorithms
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Key Techniques for Text Data Mining Word Embedding Word embedding converts words into numerical vectors in a continuous vector space, capturing
semantic relationships
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Information Retrieval Latent
Semantic Analysis Uses singular value decomposition to identify patterns and relationships in data, improving retrieval accuracy
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Text Analysis Techniques Word Embedding A technique that converts words into numerical vectors while preserving
semantic relationships
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Sound Quality Analysis Semantic Differential Scales: Listeners rate audio samples on a scale of adjectives (e
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Textual Patterns Semantic Patterns: These relate to the meanings and relationships of words and phrases within the text
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