Quick Guide: Steps To Perform Text Data Cleaning in Python
Introduction
Twitter has become an inevitable channel for brand management. It has compelled brands to become more responsive to their customers. On the other hand, the damage it would cause can’t be undone. The 140 character tweets has now become a powerful tool for customers / users to directly convey messages to brands.
For companies, these tweets carry a lot of information like sentiment, engagement, reviews and features of its products and what not. However, mining these tweets isn’t easy. Why? Because, before you mine this data, you need to perform a lot of cleaning. These tweets, once extracted can come with unwanted html characters, bad grammar and poor spellings – making the mining very difficult.
Below is the infographic, which displays the steps of cleaning this data related to tweets before mining them. While the example in use is of Twitter, you can of course apply these methods to any text mining problem. We’ve used Python to execute these cleaning steps.
