Handling Text: Document Retrieval, Classification, Sentiment Analysis

Related lectures (30)

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Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors like BERT.

Explores text handling, focusing on matrices, documents, and topics, including challenges in document classification and advanced models like BERT.

Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.

Explores Latent Semantic Indexing, a technique for mapping documents into a concept space for retrieval and classification.

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors.

Covers embedding models for document retrieval, latent semantic indexing, SVD, and topic models.

Covers the training of a binary sentiment classifier using an RNN.

Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.

Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.

Explores handling text data, deriving clean datasets from unstructured text, and various text analysis techniques.