Automated Machine learning or autoML is used for automating the complete process of machine learning for real-world problems to make the process easier and more efficient. Over the years researchers have developed ways of automating processes by developing tools like AutoKeras, AutoSklearn and even no-coding platforms like WEKA and H2o.

One such area of automation is in the field of natural language processing. With the development of AutoNLP, it is now super easy to build a model like sentiment analysis with very few basic lines of code and get a good output. With automation like these, it allows everyone to be a part of the machine learning community and does not restrict machine learning to only developers and engineers.

In this article, we will learn about what AutoNLP is and implement a sentiment analysis model with twitter dataset.

What is AutoNLP?

Using the concepts of AutoML, AutoNLP helps in automating the process of exploratory data analysis like stemming, tokenization, lemmatization etc. It also helps in text processing and picking the best model for the given dataset. AutoNLP was developed under AutoVIML which stands for Automatic Variant Interpretable ML. Some of the features of AutoNLP are:

1. Data cleansing: The entire dataset can be sent to the model without performing any process like vectorization. It even fills the missing data and cleans the data automatically.
2. **Uses feature tools library for feature extraction: **Feature Tools is another great library that helps in feature engineering and extraction in any easy way.
3. **Model performance and graphs are produced automatically: **Just by setting the verbose, the model graph and performance can be shown.
4. **Feature reduction is automatic: **With huge datasets, it becomes tough to select the best features and perform EDA. But this is taken care of by AutoNLP.

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