Gensim Python Library: Complete Guide to Topic Modeling & NLP

NLP Gensim Tutorial

Gensim is a powerful python library and it is efficient in topic modelling, text processing and vector space modelling in NLP. It is open-source library and known for memory and speed efficiency. It can discover hidden topics also build word embedding and analyze text corpora.

• It can manage large datasets through incremental training and streaming.
• Gensim supports LDA, Doc2Vec and Word2Vec.
• Mostly used for text similarity tasks, topic discovery and semantic analysis.

Table of Contents:

1. Introduction
2. Library Installation
3. Key Features
4. Application
5. Conclusion

Installation of Gensim Python Library:

You have to first download python with python.org website and install in system, simultaneously have to add path. Install the library from below command.

pip install genism

User can check the library version with below code:

import genism
print(genism.__version__)

Gensim Library Installation Video

Key features of Gensim:

• Word Embedding: It can convert word into numbers and popular models used like FastText, Word2Vec.
• Document Similarity: It can compare two documents and find similar posts, article or messages.
• Memory Efficients: It works well with large dataset.
• Topic Modelling: Gensim is famous for Latent Dirichlet Allocation, it can find hidden topics from document.
  • Example: News Article- Sports, Politics, Tech

Gensim is Used:-

• Sentiment Analysis
• Search Engine
• Chatbots
• Document Clustering
• Recommendation System

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