Text Mining

Text mining takes large amounts of unstructured data in the form of text and turns it into actionable insights. Basically, it takes unstructured text, parses the data, finds patterns within the structured data and interprets the output.

The first order of business is processing the text itself with text mining techniques that find word count frequency, sentence length and the presence (or absence) of specific words. Then a number of natural language processing techniques such as named entity recognition, text categorization, clustering, sentiment analysis, and text summarization are used to analyze the text.

There are numerous interesting text mining applications including those for business intelligence, marketing data analysis, and content optimization. The latter is particularly helpful in helping content teams create expert-level content at scale.

Data Cleaning & Text Pre-Processing in Python