Using Watson Natural Language Understanding to analyze sentiment
Last updated
Was this helpful?
Last updated
Was this helpful?
Natural-language understanding (NLU) is a subtopic of natural-language processing in artificial intelligence that deals with machine reading comprehension. In this lab, we'll use Watson Natural Language Understanding to extract keywords from a data set and analyze them for the sentiment that is expressed. We'll do this in a Jupyter notebook using Python APIs and then we'll utilize , , and to visualize this information to aid in extracting insights.
In this workshop, we will learn how to:
Create and use and instance of Watson Natural Language Understanding
Connect to Watson Natural Language Understanding using Python APIs
Perform analysis on text to derive Keywords, Sentiment, and Emotion
Depict our information graphicaly to better understand the data
If you have not already done so, make sure that you do the work for your
Click on the Navigation Menu in the upper-left corner of your instance and then click Services -> Services catalog:
![CPDaaS Navigation Menu Services catalog(../assets/images/nlu/nlu-cpdaas-service-catalog.png)
Type Natural Language Understanding in the search bar (the smart search will narrow down the choices as you type) and then click on the tile for Natural Language Understanding:
Choose the free Lite plan (unless your organization has already provisioned 1 instance of the Lite plan, in which case your subsequent plans would need to be on a paid tier). Give the service a meaningful name and click Create.
You will see your newly created NLU instance on the Services instances page. Click on the name to open the page for this NLU instance:
Either click on the Credentials tab of click on View service credentials to navigate to the Credentials page:
Click on Create Credentials. Next, click on the "down arrow" to view the newly created credentials. In the image below, you will see that I have obscured both the apikey and the url. You will need these later, so either copy them to a text document, or leave this tab open:
Click on the tabs for Extraction, Classification, Linguistics, and the Custom Entities. Notice that there are sub-tabs for each that you can investigate.
Click on the tab for Classification and the sub-tab Emotion. You can see how NLU extracts information about the emotional content of the text.
Here you can see the confidence scores for each of the various target phrases that were extracted, broken down into "Sadness", "Joy", "Fear", "Disgust", and "Anger".
In your project, click Add to project and choose Notebook:
Choose New notebook From URL. Give your notebook a name and copy the URL https://github.com/IBM/python-and-analytics/blob/master/notebooks/nlu-sentiment-analysis.ipynb. Click Create:
Spend some time looking through the sections of the notebook to get an overview. A notebook is composed of text (markdown or heading) cells and code cells. The markdown cells provide comments on what the code is designed to do.
You will run cells individually by highlighting each cell, then either click the Run button at the top of the notebook or hitting the keyboard short cut to run the cell (Shift + Enter but can vary based on platform). While the cell is running, an asterisk ([*]) will show up to the left of the cell. When that cell has finished executing a sequential number will show up (i.e. [17]).
Note that you can access the and the from the NLU Service instance Overview tab. You may wish to look at these for a more in-depth investigation of Watson Natural Language Understanding.
Visit the . Click the Analyze text button.
Now that we've seen how Natural Language Understanding and Sentiment Analysis work, let's use the in a Jupyter notebook along with our graphing and visualization tools.
Go to your project on
