Text Mining with Python for Introduction to Projec

Text mining has a long and reach history with variations of natural language processing methods for many different fields, from pharmaceutical to news sentiments. Project management is also a mature application domain since 60s and it has lots of principles, assumptions, constraints, guidance, knowledge areas, methodologies and processes for many years. One of the fruits of project management is case studies, an output that can expose the level of project management that it yields. This work is an attempt to scrutinize the output with help of what we know and probe the practicality of theories.

The libraries that were used include nearly all attributes that exist, from Gensim to Dirichlet, cosine similarity to Vader scores. Per project management perspective, maturity models were examined and from wide applicability perspective, KPM3 (Kerzner Project Management Maturity Model) was selected. Model originally has 5 Levels, from Common Languages to Continuous Improvement. Aiming to detect a duality between theory and practice, in the light of PMBOK taxonomies, we have more sticked with Level 1.
In one sentence, SMBs are more knowledgeable than we expect, but they apply less than we know.