Topic Model/Mapping
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- Latent Dirichlet Allocation (LDA)
- Natural Language Processing (NLP)
- Beautiful Soup a Python library designed for quick turnaround projects like screen-scraping
- Term Frequency–Inverse Document Frequency (TF-IDF)
- Probabilistic Latent Semantic Analysis (PLSA)
- Topic model | Wikipedia
- Hierarchical Dirichlet Process | Wikipedia
- Latent Semantic Analysis | Wikipedia
- Explicit semantic analysis | Wikipedia
- Topic Modeling with LSA, PLSA, LDA & lda2Vec | Joyce Xu
Topic modelling can be described as a method for finding a group of words (i.e topic) from a collection of documents that best represents the information in the collection. It can also be thought of as a form of text mining – a way to obtain recurring patterns of words in textual material.
In machine learning and Natural Language Processing (NLP), a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear equally in both.
Topic Map
A topic map is a standard for the representation and interchange of knowledge, with an emphasis on the findability of information. Topic maps were originally developed in the late 1990s as a way to represent back-of-the-book index structures so that multiple indexes from different sources could be merged. However, the developers quickly realized that with a little additional generalization, they could create a meta-model with potentially far wider application. The ISO standard is formally known as ISO/IEC 13250:2003.
A topic map represents information using
- topics, representing any concept, from people, countries, and organizations to software modules, individual files, and events,
- associations, representing hypergraph relationships between topics, and
- occurrences, representing information resources relevant to a particular topic.
Topic maps are similar to concept maps and mind maps in many respects, though only topic maps are ISO standards. Topic maps are a form of semantic web technology similar to RDF.
Latent Variables Role Topic Modeling
Latent variables play a central role in topic modeling, which is a technique used in Natural Language Processing (NLP) and text analysis to uncover the hidden thematic structure within a collection of documents. The latent variables in topic modeling represent the topics or themes that are not explicitly observed but are inferred from the patterns in the text data. Here's how latent variables are related to topic modeling:
- Latent Topics: In topic modeling, the fundamental idea is that each document in a corpus is a mixture of latent topics. These topics represent themes or concepts that are prevalent in the documents but are not directly observed. Each document is characterized by the distribution of these latent topics.
- Latent Variables for Words: Topic modeling also assumes that each word in a document is generated from one of the latent topics. This assignment of words to topics is governed by a set of latent variables, often referred to as "topic assignments" or "topic indicators." These latent variables indicate which topic is responsible for generating each word in a document.
- Probability Distributions: Latent variables are used to model the probability distributions of topics within documents and the distributions of words within topics. These distributions capture how likely it is for a document to contain certain topics and how likely it is for a word to appear in a topic.
- Inference and Estimation: In practice, topic modeling algorithms, such as Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA), use statistical methods to estimate the values of these latent variables. These methods aim to find the most likely assignments of topics to words and documents based on the observed text data.
