Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer and Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. The adequacy of LSA’s reflection of human knowledge has been established in a variety of ways. For example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates word–word and passage–word lexical priming data; and, as reported in 3 following articles in this issue, it accurately estimates passage coherence, learnability of passages by individual students, and the quality and quantity of knowledge contained in an essay.

Things reuired

1. Jupyter Notebook
2. Python
3. Gensim

To use this Code just download the repository & open it up in Jupyter Notebook. The code is ready for your next use, So what are you wating for? Start creating something awesome! Good Luck!

• Gensim - The main Library used
• Python - Programming Language used
• Jupyter Notebook - A web based coding enviorment

Feel free to submit pull requests to me.

• Muhammad Haseeb - Initial work - Muhammad Haseeb

This project is licensed under the MIT License - see the LICENSE file for details