With recent advancements in NLP, it’s rapidly taking over many different fields. Unsurprisingly, marketing is no exception and companies have been turning to these novel technologies in search of improving brand image and increasing sales. Yet, as is often the case with advancements in AI, there loom...

In the previous 4 blog posts, I covered the most important steps of the NLP Pipeline, data collection, text preprocessing, feature extraction, and modeling. In this blog post, I will go over the last and most important stage, model evaluation. Model evaluation is vital to understanding how effective...

In the previous blog post about modeling, I covered traditional machine learning algorithms in NLP. Recent developments in artificial intelligence have led to the emergence of a new field called deep learning. Deep learning is a subset of machine learning that utilizes neural networks that mimic the...

In the previous blog post, I covered feature extraction, the second step in the NLP Pipeline. In this post, I will go over modeling, the most complex stage in the NLP process.  The first step in modeling is choosing between using a traditional machine learning algorithm or deep learning. That determination...

In the previous blog post, I covered text preprocessing as the first step in the NLP Pipeline. In this post, I will cover the following step,  feature extraction. Feature extraction, a form of data manipulation, is the last step before building the actual NLP model. Feature extraction essentially aims...

The process of transforming raw text into a natural language processing (NLP) model that can translate text between numerous languages or generate paragraphs of human-sounding text, involves a multitude of complex stages known together as the NLP Pipeline. These include data collection, text preprocessing,...