- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
A
C
D
G
L
M
N
P
R
S
T
X
- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
Text Classification
What is Text Classification?
Text Classification, also known as text categorization or text tagging, is a technique used in machine learning and artificial intelligence to automatically categorize text into predefined classes or categories. It involves training a model on a labeled dataset, where each text example is associated with a specific class or category. The trained model can then be used to classify new, unseen texts into the appropriate categories.
How Text Classification Works
Text Classification uses various techniques from natural language processing and machine learning to analyze and understand the content of text documents. The process typically involves the following steps:
Data Preparation: The text data is collected and preprocessed, which may involve tasks such as removing stopwords, tokenization, and stemming.
Feature Extraction: The relevant features or characteristics of the text documents are extracted. This can be done using techniques like bag of words, word embeddings, or term frequency-inverse document frequency (TF-IDF).
Model Training: A machine learning model, such as a decision tree, random forest, or neural network, is trained using the labeled text data. The model learns to recognize patterns and associations between the features and the corresponding classes or categories.
Prediction: Once the model is trained, it can be used to classify new, unseen text documents by extracting their features and predicting the most appropriate class or category.
Why Text Classification is important
Text Classification brings several benefits to businesses and organizations. It enables automated analysis of vast amounts of textual data, such as customer reviews, social media posts, emails, and support tickets. By automatically categorizing this data, businesses can gain valuable insights and make data-driven decisions. Text Classification also helps in information retrieval, spam filtering, sentiment analysis, customer support, and content recommendation. It saves time and resources by reducing manual effort and improving efficiency.
The Most Important Text Classification Use Cases
Text Classification finds applications in various domains and industries. Some of the most important use cases include:
Sentiment Analysis: Determining whether a customer review or social media post is positive, negative, or neutral.
Document Classification: Classifying news articles, research papers, or legal documents into relevant topics or categories.
Spam Detection: Identifying and filtering out spam emails, messages, or comments.
Intent Classification: Classifying customer queries or requests to provide relevant responses or route them to the appropriate department.
News Categorization: Organizing news articles into categories such as sports, politics, entertainment, etc.
Text Classification is a valuable technique that can augment the capabilities of H2O's machine learning platform. By incorporating Text Classification into their workflows, H2O users can unlock additional insights and intelligence from textual data, enabling them to make more informed decisions and achieve better results. Text Classification empowers businesses to leverage the power of artificial intelligence and machine learning on textual data, thereby driving innovation and growth.