6 Practices to enhance the performance of a Text Classification Model
Introduction
A few months back, I was working on creating a sentiment classifier for Twitter data. After trying the common approaches, I was still struggling to get good accuracy on the results.
Text classification problems and algorithms have been around for a while now. They are widely used for Email Spam Filtering by the likes of Google and Yahoo, for conducting sentiment analysis of twitter data and automatic news categorization in google alerts.
However, while dealing with enormous amount of text data, model’s performance and accuracy becomes a challenge. The performance of a text classification model is heavily dependent upon the type of words used in the corpus and type of features created for classification. I used several practices to improve the results of my model.
In this article, I’ve illustrated the six best practices to enhance the performance and accuracy of a text classification model which I had used:
1.