A Test of Two Sentiment Analysis Libraries
June 17, 2021
A post by developer Alan Jones at Towards Data Science takes a close look at “Two Sentiment Analysis Libraries and How they Perform.” Complete with snippets of code, Jones takes us through his comparison of TextBlob and VADER. He emphasizes that, since human language is so nuanced, sentiment analysis is imprecise by nature. We are sure of one thing—the word “lawyer” in a customer support email is probably a bad sign. Jones introduces his experiment, and describes how interested readers might perform their own:
“So, it’s not reasonable to expect a sentiment analyzer to be accurate on all occasions because the meaning of sentences can be ambiguous. But how just accurate are they? It obviously depends on the techniques used to perform the analysis and also on the context. To find out, we are going to do a simple experiment with two easy to use libraries to see if we can find out what sort of accuracy we might expect. You could decide to build you own analyzer and, in doing so, you might learn more about sentiment analysis and text analysis in general. If you feel inclined to do such a thing, I highly recommend that you read the article by Conor O’Sullivan, Introduction to Sentiment Analysis where he not only explains the aim of Sentiment Analysis but demonstrates how to build an analyzer in Python using a bag of words approach and a machine learning technique called a Support Vector Machine (SVN). On the other hand you might prefer to import a library such as TextBlob or VADER to do the job for you.”
Jones walks us through his dual analysis of the 500 tweets found in the Sentiment140 for Academics collection, narrowed down from the 1.6 million contained in the greater Sentiment140 project. The twist it this: he had to reconcile the different classification schemas used by TextBlob and VADER. See the post for how he applies the two analyzers to the dataset and compares the results.
Cynthia Murrell, June 17, 2021
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