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Accessing text analytics resources

Version:

View and manage the resources that you created or uploaded in the process of building a machine-learning model for text analytics, such as taxonomies for topic detection and sentiment lexicons for sentiment analysis and intent detection.

  1. In the navigation pane of Prediction Studio, click Data Resources .

  2. Optional:

    To access a taxonomy, select a resource of type Taxonomy and perform one of the following actions:

    • To test the taxonomy with real-life text samples, click Test.
    • To view the .csv file that contains the topic hierarchy with the associated keyword configuration, click Download taxonomy.
    • For model-based taxonomies: To download the binary file that contains the taxonomy model, click Download model.
    • To update the existing taxonomy by modifying the associated keyword configuration or training a topic detection model for a specific language, click Update language.
    • To modify the resource status, in the Details section on the right, expand the Status drop-down and select a status type.
  3. Optional:

    To access a sentiment lexicon, select a resource of type Lexicon and perform one of the following actions:

    • To upload a .csv file that contains a sentiment lexicon, click Import.
    • To download a .csv file that contains this sentiment lexicon, click Export.
    • To modify the resource status, in the Details section on the right, expand the Status drop-down and select a status type.
  4. Click Save.

  • Managing data

    Create and manage data sets, Interaction History summaries, and other resources. Make sure that you identify the data that correlates to your business use case and that is aligned with the use problem that you want to solve.

  • Creating data sets

    You can create a data set for storing data that is important for the business use case that you want to solve. To accommodate various use cases, you can create multiple types of data sets, for example, a Monte Carlo data set that simulates customer records, a social media data set for extracting Facebook posts and so on.

  • Creating summaries

    You can create an Interaction History summary data set that is based on your input criteria. For example, you can create a summary of all Interaction History records for a customer that shows all accepted offers within the last 30 days. You can use Interaction History summaries to filter out irrelevant offers (for example, do not display this advertisement to a specific customer if that customer has already viewed it within this month).

  • Sentiment lexicons

    A sentiment lexicon is a list of semantic features for words and phrases. Use lexicons for creating machine learning-based sentiment and intent analysis models.

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