Published in 2013, “Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank” presented the Stanford Sentiment Treebank (SST). SST is well-regarded as a crucial dataset because of its ability to test an NLP model’s abilities on sentiment analysis. Let’s go over this fascinating dataset.

Predicting levels of sentiment from very negative to very positive (- -, -, 0, +, ++) on the Stanford Sentiment Treebank. Image credits to Socher et al., the original authors of the paper.

The task. SST handles the crucial task of sentiment analysis in which models must analyze the sentiment of a text. For example, this could come in the form of determining whether restaurant reviews are positive or negative. Here are some made-up examples that display a range of positivity and negativity in their sentiment:

This was the worst restaurant I have ever had the misfortune of eating at.

The restaurant was a bit slow in delivering their food, and they didn’t seem to be using the best ingredients.

This restaurant is pretty decent— its food is acceptable considering the low prices.

This is the best restaurant in the Western Hemisphere, and I will definitely be returning for another meal!

Based on these examples, sentiment analysis may seem like an easy task. However, there are lots of challenging nuances that can make it difficult to accurately analyze a phrase’s sentiment. Linguistic anomolies such as negation, sarcasm, and using negative terms in a positive way are especially difficult for NLP models to handle.

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