Editor’s note: Benjamin Batorsky, PhD is a speaker for ODSC East 2023. Be sure to check out his talk, “Bagging to BERT – A Tour of Applied NLP,” there! It’s always good to start a blog post with a joke (even if it’s not a very good one): Why is... Read more

Editor’s note: Ben is a speaker for ODSC West this November 1st-3rd. Be sure to check out his talk, “Bagging to BERT – A Tour of Applied NLP,” there! Every two days, we generate as much data as was produced from the start of human history to 2003.  These data... Read more

Artificial intelligence emotion and sentiment analysis has come a long way over the years and is on track to revolutionize the AIs of the future. Some wonder if it can ever truly understand human emotions, but computer scientists are focusing on training AI to recognize these feelings correctly. The technology... Read more

The GloVe model came out in 2014, a year after the Word2Vec paper came out. The GloVe and Word2Vec models are similar as the embeddings generated for a word are determined by the words that occur around it. However, these context words occur with different frequencies. Some of these context words appear more frequently... Read more

Data might be supreme, but your customers are more than just data points. When they’re affected by your product, good or bad, you need to know to move forward or to pivot. Sentiment analysis has come a long way since the early days and can now predict with accuracy the... Read more

This video discusses the use of active learning, deep learning, Bayesian inference, and causality in Project Feels. This project, developed by the Data Science Group at the New York Times, sought to predict how likely a given article was to evoke a range of emotions. Thus project crowdsourced data from... Read more

Sentiment analysis is located at the heart of natural language processing, text mining/analytics, and computational linguistics. It refers to any measurement technique by which subjective information is extracted from textual documents. In other words, it extracts the polarity of the expressed sentiment in a range spanning from positive to negative.... Read more

Sentiment analysis algorithms understand language word by word, estranged from context and word order. But our languages are subtle, nuanced, infinitely complex, and entangled with sentiment. They defy summaries cooked up by tallying the sentiment of constituent words. Unsophisticated sentiment analysis techniques calculate sentiment/polarity by matching words back to a... Read more

First of all, I would like to point out that the skill of building MVP and microservices for a data scientist is extremely useful! When you can build a prototype and test it in a working environment it just feels so much better and allows you to better understand your... Read more

This is a joint article authored in collaboration between Kannan Sankaran and Win Suen. The Problem Over the past few years, there has been burgeoning interest in neural networks from data science and engineering communities. The advent of ever larger datasets, efficient commodity hardware, and powerful open source libraries have... Read more