Text Analytics Using PostgreSQL
In addition to performing analytics using complex data structures within PostgreSQL, you can also make use of the non-numeric data available. Often, the text contains valuable insights. For instance, you can imagine a salesperson keeping notes on prospective clients, such as "Very promising interaction, the customer is looking to make a purchase tomorrow," is valuable data, as does this note: "The customer is uninterested. They no longer have a need for the product." While this text can be valuable for someone to manually read, it can also be valuable in the analysis. Keywords in these statements, such as "promising," "purchase," "tomorrow," "uninterested," and "no," can be extracted using the right techniques to try to identify top prospects in an automated fashion.