Since the beginning of time, written texts have been a means to communicate, express and document something of significance. Even in the modern age, they have proven a lot of times that an individual writing style can be a defined aspect of one’s psyche. Ever since social media emerged, Micro-blogging became the new form of writing, expressing, or documenting an event. This also gave rise to a lot of unstructured data and with it a need to understand that data. This is where text classification can be put to our advantage.
Text classification is nothing but classifying unstructured text data into various categories such as Technologies, Sports, Entertainment, and so on.
To further simplify how we use text classification, let’s consider an example:
You have a product that was launched a while ago and you have also kept track of the reviews that the product got on all the platforms across the internet. Now what you have is unstructured text data.
To perform text classification, you can make use of two approaches:
- You can either make a few rules where a collection of words will decide the sentiment of the input text. This approach can be useful for a handful of data but analyzing extensive sets of data is neither efficient nor cost-effective.
- A better approach than the first approach is making use of Natural Language Processing(NLP) and classification using Machine Learning. For this, we should treat the classifier with a separate class labeled data. And using this model we can do classification only for data. When we give input to the classifier, we will get an output with a class category based on our trained model telling us if the review was average, good, or bad. This way, text classification on user reviews can help us improve user experience.
Data is the new fuel, thus Even bad reviews can help us identify the attributes which can help us improve our upcoming campaigns. Businesses and organizations are following this trend to understand user sentiment and user behavior. We can also use text classification for applications like Spam detection in emails, targeting customer needs, etc. In this day and age where we generate data every second of the day, text classification becomes an asset for any organization.
