Advanced Analytics: Text, Time Series, and Decision Trees

In this lesson, we’ll learn the next five most important methods for analyzing data and how to use them to set up API product analytics.

Text analysis

Text analysis is a way to get information from text data by using techniques such as Natural Language processing (NLP) and Machine Learning (ML). It is often used to find patterns and trends in text data and to classify, cluster, and summarize text data.

Text analysis can be used in a number of ways to look at data about how APIs are used. For example, we could use text analysis to:

• Analyze the text data generated by API usages, such as error messages, log files, and user feedback: In turn, this could help us identify trends and patterns in API usage, such as which API calls are most popular, are experiencing the most errors, and are using the most resources.

• Classify API usage data into different categories based on the content of the text data: For example, we might classify API calls as successful or unsuccessful based on the text of the error messages that are generated.

• Cluster API usage data into groups based on the similarity of the text data: This could help us identify groups of API calls that are similar in some way, such as API calls that are experiencing similar errors or being used by similar types of users.

• Summarize API usage data by extracting key points or trends from the text data: This could help us quickly understand the most critical aspects of the API usage data, such as the most common types of API calls or the most common reasons for API failures.