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Text Classification is a Natural Language Processing (NLP) task where AI algorithms automatically assign predefined categories to text data. It involves analyzing textual content and categorizing it based on content, tone, or purpose.
Text classification helps in organizing, structuring, and filtering text data for efficient information retrieval and automated processing. It relies on machine learning models trained on labeled datasets to recognize patterns in language.
Common approaches include Naive Bayes, Support Vector Machines (SVM), decision trees, and deep learning models like transformers. Categories can include sentiment (positive/negative), topic (finance/health), spam detection, or intent (query/complaint).
AI enhances accuracy and scalability by learning contextual nuances and improving classification performance across large volumes of data.
Text classification is the process of assigning categories to text using AI models trained on labeled datasets.
Common algorithms include Naive Bayes, SVM, decision trees, and deep learning models such as CNNs, RNNs, and transformers.
Applications include spam detection, sentiment analysis, topic tagging, intent recognition, and customer support routing.
With high-quality data and advanced models, text classification accuracy can exceed 90% in many applications.
Yes, multilingual models like XLM-RoBERTa and mBERT can classify text across multiple languages with high accuracy.
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