Quick guide:steps to Perform Text Data cleaning in Python

Quick guide:steps to Perform Text Data cleaning in Pythonintroduction

Twitter has become a 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 character tweets have now become a powerful tool for customers/users to directly convey messages to brands.

For companies, these tweets carry a lot of information as sentiment, engagement, reviews and features of its products an D what is 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 ve Ry difficult.

Below is the infographic, which displays the steps of cleaning this data related to tweets before mining them. While the example on use are of Twitter, you can of course apply these methods to any text mining problem. We ' ve used Python to execute these cleaning steps.

Download the PDF Version of this infographic and refer the Python codes to perform Text Mining and follow your ' Next Steps ... '-Download here

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Quick guide:steps to Perform Text Data cleaning in Python