A Comprehensive Guide to Understand and Implement Text Classification in Python

Improving Text Classification Models

While the above framework can be applied to a number of text classification problems, but to achieve a good accuracy some improvements can be done in the overall framework. For example, following are some tips to improve the performance of text classification models and this framework.

1. Text Cleaning : text cleaning can help to reducue the noise present in text data in the form of stopwords, punctuations marks, suffix variations etc. This article can help to understand how to implement text classification in detail.

2. Hstacking Text / NLP features with text feature vectors : In the feature engineering section, we generated a number of different feature vectros, combining them together can help to improve the accuracy of the classifier.

3. Hyperparamter Tuning in modelling : Tuning the paramters is an important step, a number of parameters such as tree length, leafs, network paramters etc can be fine tuned to get a best fit model.

4. Ensemble Models : Stacking different models and blending their outputs can help

 

 

 

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