Concept

Sentiment analysis

Related lectures (34)
Link-Based Ranking: PageRank
Explores link-based ranking with a focus on PageRank algorithm and its practical application in web search engines.
Natural Language Processing Basics
Covers the basics of Natural Language Processing, including tokenization, part-of-speech tagging, and embeddings, and explores practical applications like sentiment analysis.
Word Embeddings: Modeling Word Context and Similarity
Covers word embeddings, modeling word context and similarity in a low-dimensional space.
Deep Generative Models: Part 2
Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.
Binary Sentiment Classifier Training
Covers the training of a binary sentiment classifier using an RNN.
Text Data Processing: Basics & Techniques
Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.
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.
Text Data Analysis: Basics and Techniques
Introduces the basics of text data analysis, covering document retrieval, classification, sentiment analysis, and topic detection using preprocessing techniques and machine learning models.
Handling Text Data: Document Retrieval and Classification
Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors.
Text Data Analysis: Techniques and Applications
Explores handling text data, deriving clean datasets from unstructured text, and various text analysis techniques.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.