Reviewing the sentiment analysis model
When a model is created, analyze its accuracy in the Model analysis step.
After the model creation process finishes, click Download report to view the outcome of sentiment analysis of the testing sample.You can view the following accuracy measures:
- Sentiment analysis results.
- Manual (predicted) and machine (actual) outcome comparison.
- True positive, precision, recall, and F-score measures.
To view the detailed sentiment analysis data, click the Expand icon next to the model that you created.You can view the following data:
- In the Category summary tab, view the predicted (manual) and actual (machine) outcome comparison of the assigned classification values. Additionally, you can view true positive, precision, recall, and F-score measures.
- In the Test results tab, view the classification analysis of the records in the testing sample. You can view the actual (machine), predicted (manual) outcome as well as whether the actual and manual outcomes match.
- Analyzing natural language
Effortlessly analyze and extract meaningful information from large volumes of text with the use of text analytics. Based on your findings, you can further improve business performance and customer experience.
- Determining the emotional tone of text
Sentiment analysis determines whether the opinion that the writer expressed in a piece of text is positive, neutral, or negative. Knowledge about customers' sentiments can be very important because customers often share their opinions, reactions, and attitudes toward products and services in social media or communicate directly through chat channels.
- Text analytics accuracy measures
Models predict an outcome, which might or might not match the actual outcome. The following measures are used to examine the performance of text analytics models. When you create a sentiment or classification model, you can analyze the results by using the performance measures that are described below.