Python

Closeness centrality can tell us how to find important nodes in a network. The important nodes could disseminate information to many nodes or prevent epidemics, or hubs in a transportaion network, etc.

In this post I will briefly share the connectivity related concepts and functions of clustering coefficient, distance measures, and connection robustness.

Networks is a set of objects (nodes) with interconnections (edges). Many complex structures can be represented by networks. It is everywhere in different forms. For example, family network, Facebook communication network, subway network, food web, etc.

Topic modeling is a useful tool for people to grasp a general picture of a long text document. Compared with LSTM or RNN, topic model is more or less for observatory purpose rather than prediction. In this post I will share the measure of similarity among words, the concept of topic modeling and its application in Python.

In the previous two posts, I have shared basic concepts and useful functions of text mining and NLP. In this third post of text mining in Python, we finally proceed to the advanced part of text mining, that is, to build text classification model. In this post I will share the main tasks of text classification. Two useful classification models, their implementation in Python and methods of improving classification performance.

In this post I will share what are the basic NLP tasks and how to deal with different tasks by using the powerful NLTK library in Python.

In this exercise, we'll be working with messy medical data and using RegEx in Python to extract dates of different formats. The goal of this exercise is to correctly identify all of the different date variants encoded in this dataset and to properly standardize and sort the dates.