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๐Ÿฅธ๐Ÿฅธ
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May 21, 2022 1:33 PM
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May 24, 2023 12:00 AM (GMT+1)
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In this block, the attention revolves around the analytical and computational strategies to model the meaning included in a corpus of text: i) human-annotated dictionaries, and ii) word vectors. A series of examples and Python scripts show how to leverage human-annotated dictionaries and learn word vectors using text corpora regarding organizations and markets
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May 24, 2023
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Open the circle: learn core NLP aspects
Get the decks & videos โฌ‡๏ธย ๐Ÿ—‚ย ย ๐ŸŽฅโ€‹
Readings for NLP nerds ๐Ÿค“โ€‹
On BoW
Zhang, Yin, Rong Jin, and Zhi-Hua Zhou. "Understanding bag-of-words model: a statistical framework."ย International Journal of Machine Learning and Cybernetics ย 1, no. 1 (2010): 43-52
On TFIDF
Robertson, Stephen. "Understanding inverse document frequency: on theoretical arguments for IDF."ย Journal of documentation ย (2004).
Test your learning
NLP application examples
The examples I report cut across different domains, such as marketing, operations, and people analytics. Hopefully, youโ€™ll find these materials helpful. A good number of the papers are freely accessible from the publisherโ€™s website. For the remaining papers, give it a try with Google Scholar โ€” good luck!
Bag of words applications
TFIDF applications
Make things happen with Python
Off-the-shelf Python scripts
Problem set
Close the circle by analyzing a case study
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