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How do you improve text classification accuracy?
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:
- Domain Specific Features in the Corpus.
- Use An Exhaustive Stopword List.
- Noise Free Corpus.
- Eliminating features with extremely low frequency.
- Normalized Corpus.
What is a good accuracy for NLP?
You could say a good model has an accuracy above 80%. NLP is a broad application area of IA, and encloses several tasks like: Text classification. Information extraction.
How to use machine learning for text classification?
This is achieved with a supervised machine learning classification model that is able to predict the category of a given news article, a web scraping method that gets the latest news from the newspapers, and an interactive web application that shows the obtained results to the user. This can be seen as a text classification problem.
What does 95% accuracy mean in text classification?
This means that the dataset contains an approximately equal portion of each class. For example, if we had two classes and a 95% of observations belonging to one of them, a dumb classifier which always output the majority class would have 95% accuracy, although it would fail all the predictions of the minority class.
Why do human annotators make mistakes in text classification?
Human annotators make mistakes when classifying text data due to distractions, fatigue, and boredom, and human subjectivity creates inconsistent criteria. Machine learning, on the other hand, applies the same lens and criteria to all data and results. Once a text classification model is properly trained it performs with unsurpassed accuracy.
Why do I write articles about text classification?
The motivation behind writing these articles is the following: a s a learning data scientist who has been working with data science tools and machine learning models for a fair amount of time, I’ve found out that many articles in the internet, books or literature in general strongly focus on the modeling part.