In this article, we'll see how to use SingleStore and WebAssembly to perform sentiment analysis of Stack Overflow comments. We'll use some existing WebAssembly code that has already been prepared and hosted in a cloud environment.

The notebook file used in this article is available on GitHub.

In this article, we'll take an existing SingleStore Labs project and demonstrate the ease with which it can be deployed and run on SingleStore Cloud. The original project was developed before SingleStore provided support for notebooks in the cloud portal. We'll see the ease with which we can migrate and consolidate the code.

A previous article showed the steps to create a free SingleStore Cloud account. We'll use the Standard Tier, select Google Cloud (GCP), and take the default names for the Workspace Group and Workspace.

We'll download the notebook from GitHub.

From the left navigation pane in the SingleStore cloud portal, we'll select DEVELOP > Data Studio.

In the top right of the web page, we'll select New Notebook > Import From File. We'll use the wizard to locate and import the notebook we downloaded from GitHub.

After checking that we are connected to our SingleStore workspace, we'll run the cells one by one.

We'll begin by installing the necessary libraries and importing dependencies.

We'll now create a link to a Google Cloud Storage (GCS) bucket for our Stack Overflow data and WebAssembly files:

Next, we'll create the table to store the Stack Overflow comments:

And we'll now create a Pipeline to ingest those comments into the table:

We'll also check how many files have been loaded so far, as follows:

It may take a few minutes to complete the data loading. We'll keep re-running the above command until we see the as Loaded.

If we now check the number of rows in the comments table:

We can see that the values have changed, showing a stronger positive sentiment expressed by capitalization.

Now, we'll use the sentiment function over the Stack Overflow data. The following query categorizes comments by their , calculates the positive and negative sentiment ranges for each bucket, and filters out buckets that do not meet specific thresholds for positive and negative sentiments or a minimum comment count:

We could save the result of the query in a variable and use it from Python. Alternatively, we could run the query as follows:

Figure 1: Comment Score vs. Sentiment Polarization

Figure 1 visually explores how positive and negative sentiment scores vary with comment scores. It can help identify whether comments with higher scores tend to have more polarized sentiment (either more positive or negative) and if there's a general trend or correlation between comment scores and sentiment polarity.

In this article, we've used several very useful SingleStore features, such as Pipelines to ingest data from an external source, and an external WebAssembly function loaded into the database to perform sentiment analysis. We've also been able to run both SQL and Python code from the cloud portal without the need to use any other tools.