Difference between revisions of "Probabilistic Latent Semantic Analysis (PLSA)"
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* [http://www.crummy.com/software/BeautifulSoup/ Beautiful Soup] a Python library designed for quick turnaround projects like screen-scraping | * [http://www.crummy.com/software/BeautifulSoup/ Beautiful Soup] a Python library designed for quick turnaround projects like screen-scraping | ||
* [[Term Frequency–Inverse Document Frequency (TF-IDF)]] | * [[Term Frequency–Inverse Document Frequency (TF-IDF)]] | ||
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| + | PLSA is a probabilistic generative model used for topic modeling in text data. It is an extension of Latent Semantic Analysis that introduces a probabilistic framework to the topic modeling problem. How PLSA Works: In PLSA, it is assumed that documents are generated through a probabilistic process. Specifically, it assumes that there are latent topics, and each document is a mixture of these topics. Each word in a document is generated from one of these topics with a certain probability. Applications: PLSA is mainly used for discovering topics in large document collections. By analyzing the word-topic and topic-document distributions learned by PLSA, you can identify the prevalent themes or topics within the corpus. | ||
<youtube>BJ0MnawUpaU</youtube> | <youtube>BJ0MnawUpaU</youtube> | ||
<youtube>vtadpVDr1hM</youtube> | <youtube>vtadpVDr1hM</youtube> | ||
Revision as of 09:35, 16 September 2023
Youtube search... ...Google search
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- Beautiful Soup a Python library designed for quick turnaround projects like screen-scraping
- Term Frequency–Inverse Document Frequency (TF-IDF)
PLSA is a probabilistic generative model used for topic modeling in text data. It is an extension of Latent Semantic Analysis that introduces a probabilistic framework to the topic modeling problem. How PLSA Works: In PLSA, it is assumed that documents are generated through a probabilistic process. Specifically, it assumes that there are latent topics, and each document is a mixture of these topics. Each word in a document is generated from one of these topics with a certain probability. Applications: PLSA is mainly used for discovering topics in large document collections. By analyzing the word-topic and topic-document distributions learned by PLSA, you can identify the prevalent themes or topics within the corpus.
