Gensim: 100 Tips and Strategies for Text Analysis in Natural Language Processing

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Gensim is a Python library for topic modeling, document similarity analysis, and other natural language processing tasks. Here are 100 tips for working with Gensim:

1. Installation and Import:

1. Install Gensim with pip install gensim.
2. Import Gensim in your Python script or Jupyter Notebook with import gensim.

2. Document Representation:

1. Represent documents as bags-of-words with gensim.corpora.Dictionary.
2. Convert documents to sparse vectors with doc2bow method.

3. Corpus Creation:

1. Create a corpus from a list of documents using gensim.corpora.MmCorpus.serialize.
2. Use gensim.corpora.MmCorpus for efficient memory-mapped access to large corpora.

4. TF-IDF Model:

1. Build a TF-IDF model with gensim.models.TfidfModel.
2. Transform documents to TF-IDF space with tfidf_model[doc].

5. Latent Semantic Analysis (LSA):

1. Apply Latent Semantic Analysis (LSA) with gensim.models.LsiModel.