Definition class of text analytics Decision Data rules
Use the following automaticallygenerated frameworks for Decision Data rules to quickly create or update your text analytics resources, such as models and lexicons.When creating Decision Data rules that contain the resources that are used in text analytics, you can use an updated, automatically generated framework for each class of decision data that is supported in text analysis. With the automatically generated framework for each type of text analytics Decision Data rule, you can quickly create or update the records, without having to edit the default Decision Data rule form fields.
You can create Decision Data rules that contain the following types of text analysis resources:
- Entity extraction models
- Entity extraction rules
- Intent models
- Classification models
- Sentiment models
- Sentiment lexicons
Entity extraction models
Create decision data that contains entity extraction rules in the Data-NLP-EntityModels definition class. Entity extraction models detect entities whose names are not limited to certain patterns or dictionaries. Entity extraction models can detect names of organizations, brands, people, and so on. See the following figure for reference:
Entity extraction rules
Create decision data that contains entity extraction rules in the Data-NLP-Rule definition class. Entity extraction rules detect entities whose names match a certain pattern or are part of a dictionary. Entity extraction rules can detect names of hardware or software products, identification numbers, emails, dates, and so on. You upload entity extraction rules as Apache Ruta scripts. See the following figure for reference:
Intent analysis models
Create decision data that contains intent analysis models as part of the Data-NLP-Intent definition class. Intent analysis models determine whether the content (social media posts, comments, tweets, emails, instant messages, and so on) that you analyzed in your application was produced with an underlying intention. See the following figure for reference:
Sentiment analysis models
Create decision data that contains sentiment analysis models in the Data-NLP-SentimentModels definition class. Sentiment analysis models determine whether the analyzed text expresses a negative, positive, or neutral opinion.
As part of creating or updating the rule, you can build sentiment analysis models by using a wizard. When you update an existing model for a specific language, that model is replaced by the newly created model when the creation process finishes. In a single Decision Data rule, you can have one model for each supported language.
See the following figure for reference:
For more information about the wizard for sentiment model creation, see Determining the emotional tone of text.
Classification analysis models
Create decision data that contains sentiment analysis models and taxonomies for rule-based classification analysis in the Data-NLP-Taxonomy definition class. Either by creating a classification analysis model or by using rule-based classification analysis based on taxonomies, you can determine the categories to which a text unit should be assigned. As in the case of sentiment analysis, you can use a Decision Data rule to build classification analysis models. Additionally, you can upload .csv, .xls, or .xlsx files that contain taxonomies for rule-based classification analysis.
Create decision data that contains sentiment lexicons in the Data-NLP-SentimentLexicon definition class. Sentiment lexicons contain words or phrases that are assigned a sentiment value.