Topic Modeling System
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A Topic Modeling System is a data analysis system (that applies a topic modeling algorithm to solve a topic modeling task.
- Context:
- It can range from (typically) being a Data-Driven Topic Modeling System to being a Heuristic Topic Modeling System.
- It can range from being a Text Item Topic Modeling System to being a Transaction Database Topic Modeling System.
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- It is widely used in fields like legal text analysis, research article categorization, and content recommendation systems.
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- Example(s):
- Meme Topic Tracker that monitors and categorizes topics in social media posts.
- Mallet Topic Modeling System[1], an open-source tool that applies various topic modeling techniques to large text corpora.
- TopMost, a toolkit for performing and evaluating different types of topic modeling, including dynamic and cross-lingual models.
- OCTIS, a Python package that simplifies the process of comparing and optimizing topic models.
- Top2Vec, an algorithm that finds dense clusters of topics using word embeddings, with capabilities for visualizations and interactive exploration.
- GuidedLDA, a semi-supervised version of LDA that allows for the incorporation of predefined topics using keywords.
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- Counter-Example(s):
- a Clustering System that groups items based on similarity without specifically uncovering underlying topics.
- a Keyword Extraction System that identifies important words or phrases in a text but does not infer topics from them.
- a Classification System that assigns predefined labels to data rather than discovering emergent themes.
- See: Generative Modeling System, Frequent Pattern Mining System.