Lecture

Text Data Analysis: Basics and Techniques

Related lectures (32)

Text Data Processing: Basics & Techniques

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

Text Data Analysis: Techniques and Applications

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

Handling Text: Document Retrieval, Classification, Sentiment Analysis

Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.

Document Retrieval and Classification

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

Handling Text: Document Retrieval & Classification

Explores document retrieval, classification, sentiment analysis, and topic detection in text analysis using supervised learning and bag-of-words models.

Information Retrieval Basics

Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.

Information Retrieval Basics

Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.

Text Handling: Matrix, Documents, Topics

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

Handling Text Data: Document Retrieval and Classification

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

Latent Semantic Indexing: Concepts and Applications

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

Natural Language Processing

Introduces Natural Language Processing, covering text preprocessing, sentiment analysis, and topic analysis, with a focus on building a climate change risk index.

Latent Semantic Indexing

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

Information retrieval: vector space

Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.

Embedding Models: Concepts and Retrieval

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

Vector Space Semantics (and Information Retrieval)

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

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Binary Sentiment Classifier Training

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

Latent Semantic Indexing: Concepts and Applications

Explores latent semantic indexing, vocabulary construction, document matrix creation, query transformation, and document retrieval using cosine similarity.

Data Mining: Introduction

Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.

Information Retrieval: Fagin's Algorithm

Covers the implementation of Fagin's algorithm for information retrieval, focusing on efficient document retrieval.