Analyzing your data with the Content Mining application
Use the Discovery Content Mining application to analyze your data. The application shows subsets of your information in visualizations that can help you to find patterns, trends, and anomalies.
Only users of installed deployments (IBM Cloud Pak for Data) or Enterprise and Premium plan managed deployments can use the Content Mining application.
Overview video
Video transcript
Watson Discovery Content Mining Project presented by Stuart Strolin. (Music intro) The purpose of this video is to familiarize you with the content mining project in Watson Discovery.
Content mining is one of the primary use cases for Watson Discovery and is used for analyzing and exploring both structured and unstructured data to find insights and extract hidden meaning. It is used by both the citizen analyst and the data scientist.
The content mining project can be used for all types of analysis because the user interface is not specific to a particular industry or set of data.
In this scenario, you are an analyst for a fictitious automobile company. Operational reports have alerted the company to an unusual accident rate for one of their cars. Your job is to find out why.
Using the content mining project, you begin your analysis by looking at the unstructured data from the national motor vehicle incident reports. You are presented with an interface that allows you to select the car model and begin your analysis (on the Collections page). In this case, you are interested in the Hill Walker. You could type that information into the search section at the start of the page. But it's easier just to click on the item. You can add as many search terms and conditions as you like. But in reality, you want to let the application guide your analysis.
What you see now is the navigation view (in Guided mode). It keeps track of your analysis and provides options for next steps. It also provides a count of the number of documents that match your current state of analysis. In this small collection, the number of documents relating to the Hill Walker is only 51. In a production data set, the number would usually be much larger. Analyzing trends and anomalies is often a good way to start as it allows you to see if anything seems out of the ordinary.
Immediately, you notice that the Hill Walker has problems in December and January. You decide to investigate further by narrowing this initial exploration to just the month of December.
Notice how the navigation view at the top always keep you informed of where you are in your analysis. Next, you select Analyze cause and characteristics because you are interested in why things are happening.
You notice that words like 'snow' and 'brake' are highlighted together (in the Part of Speech section), so you add these to your analysis.
The Content Miner project has narrowed your investigation to a small number of complaints that can be easily read. (clicks Show Documents)
The common theme here is that there is an unexpected problem with the way the brakes are working in snowy conditions. You now have the information you need to ask the engineering department to perform a detailed inspection of the braking system and determine why it is not working as expected in snowy conditions.
In this demonstration, you saw how a citizen analyst using Watson Discovery and content mining can easily discover hidden meaning in unstructured text. (list of features, functionality, and use cases)
What will you do with Watson Discovery? (Music outro)
How it works
To analyze your data, you use facets. Facets give you a way to slice your data and visualize a subset of information so it is easier to comprehend.
From the data analysis page for your collection, you can choose for the data to be shown in one of the following views:
- Facets
- Shows facets that are derived from from annotations that are added to your documents by enrichments that are applied to your documents. Enrichments can include built-in Natural Language Processing enrichments, such as Part of Speech or Entities. They can also include custom enrichments that you add, such as dictionaries, regular expression patterns, and machine learning models.
- Metadata facets
- Shows facets that are derived from your data. When you add files to a collection, Discovery analyzes and indexes the data. Annotations are added to identify content types and are shown as metadata facets. The best metadata facets result when you ingest structured data, such as records from a CSV file. The maximum length of a metadata facet is 256 characters.
- Custom
- Shows only the facets that you choose to add to the view. You can add a mix of enrichment-derived and content-derived facets to your custom view.
When you create a Content Mining project type, the Part of Speech facet is applied to your data automatically. This facet is a great place to start because it is valid for all data, no matter the subject. The output gives you a quick look at the terminology that is most common in the data.
From this starting point, you can determine other ways to filter the data that might be useful.
If your data consists of traffic reports, for example, the Part of Speech facet might show that high frequency keywords include terms such as engine, brake, fire, smoke, and spark. Given this common terminology, you can create dictionaries to help you categorize and filter the data. The keywords from the example might lead you to create the following dictionaries:
componentdictionary for terms such as engine and brakephenomenondictionary for terms such as fire, smoke, and spark
When you apply the dictionary enrichment to your data, it generates annotations. You can think of annotations as tags that you add to words or phrases, where the tag categorizes or identifies the meaning of the word or phrase. The resulting annotations function as new facets that you can use to filter and dissect your data further.
With your new component and phenomenon facets, for example, you can look for correlations between components and phenomena that are involved in traffic incidents.
Digging deeper
To dig even deeper into your data, apply or create AI models that can find different types of information in your documents. You can apply built-in natural language processing models, such as the Entities enrichment that can recognize mentions of commonly known things, such as business or location names and other types of proper nouns. Or you can apply a custom model that recognizes terms and categories that are unique to your data.
Getting started
Before you can use the application, you must create a Discovery Content Mining project. After the project is created and data is uploaded, you can open the Content Mining application.
For more information, see Creating projects.
Of course, you can't get out useful insights if you don't put the right type of information in. Be sure to include consistent data. If you want to find trends over time, your data must include data points that specify a date.
Data that is submitted in CSV file format is optimal. For a sample of a CSV file that provides interesting analysis capabilities, see Analyzing CSV files.