Supplemental information by the authors of the article "Problems and prospects of hybrid learning in Higher Education"N/

Bidarra, José

Rocio, Vitor

English

Repositório Aberto da Universidade Aberta

Latent Dirichlet Allocation (LDA)  (Supplemental information generated by ChatGPT and edited by the authors of the article Problems and prospects of hybrid learning in Higher Education)   Latent Dirichlet Allocation (LDA) is a generative probabilistic model that is commonly used for topic modeling. It is a method for uncovering the latent topics that underlie a set of documents.  The basic idea behind LDA is that each document is a mixture of a small number of latent topics, and each topic is a mixture of a small number of latent words. The model represents each document as a probability distribution over topics and each topic as a probability distribution over words.  The LDA algorithm works as follows: 1. Initialize the topic assignments for each word in the documents randomly. 2. For each word in each document, update its topic assignment by determining the probabilities of the word belonging to each topic, given the proportion of words in the document assigned to that topic, and the proportion of assignments of words to that topic over all documents. 3. Repeat step 2 a fixed number of times or until convergence.  The LDA algorithm can be trained using various inference methods such as variational inference and collapsed Gibbs sampling.  The Latent Dirichlet Allocation (LDA) algorithm can be applied to text mining in several steps: 1. Text pre-processing: This step involves cleaning and preparing the text data for analysis. This includes tasks such as removing stop words, punctuation, and special characters, lowercasing, stemming, and tokenizing the text. 2. Creating a document-term matrix: After pre-processing the text data, the next step is to create a document-term matrix, also known as a bag-of-words representation. This matrix is a sparse matrix where each row represents a document and each column represents a word. The entries in the matrix are the frequency of each word in the corresponding document. 3. Applying LDA: Once the document-term matrix is created, LDA can be applied to it. The number of topics (k) should be specified before running the algorithm. The algorithm will then output a set of topics, where each topic is a probability distribution over the words in the vocabulary. 4. Interpreting the results: The output of the LDA algorithm can be interpreted by examining the most probable words in each topic. These words can give an idea of what the topic is about. Additionally, the documents can be assigned to the most likely topic by examining the topic distribution for each document. 5. Visualization: The results of LDA can be visualized using various techniques such as word clouds, heat maps, and bi-plots. These visualizations can help to understand the topics, terms, and relationships between them.  Examples of applications of LDA include: • Identifying latent topics in customer reviews of products or services, • Automatic summarization of news articles, • Discovery of latent topics in scientific literature, • Identification of latent topics in social media data, • Clustering of customer queries in customer service centers.   Current references for LDA include "Topic Modeling: A Probabilistic Perspective" by David Blei and colleagues, "Probabilistic Topic Models" by David Blei and Andrew Ng, and "Latent Dirichlet Allocation" by David Blei and colleagues.                                  José Bidarra, Vítor Rocio, 2023  

Latent Dirichlet Allocation (LDA)

https://repositorioaberto.uab.pt/bitstream/10400.2/13245/1/Latent%20Dirichlet%20Allocation_rev.pdf

Latent Dirichlet Allocation (LDA)

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Repositório Aberto da Universidade Aberta