Topic Extraction

• Getting started
  • Installation
  • Overview
  • Recipes
• Analyses
  • Language Detection
  • Sentiment Analysis
  • Topic Extraction
  • Text Summarization
  • Deep Categorization
• Troubleshooting
  • Errors and logs

Topic extraction extracts named entities (people, organization, etc.), concepts, money expressions and quantities from a text. These elements have an ontology type associated that gives a more detailed description of what they are. The types are defined by MeaningCloud's ontology, although it can be customized using user-defined dictionaries.

These user-defined dictionaries can be defined in the dictionary console. There you can add new entries that follow our ontology, or define your own.

These are the settings for this recipe:

• Input parameters:
  • Text column: the column names available in the dataset used as input source will be loaded, so you can select the one with the texts to analyze.
  • Language: language of the text to analyze.
• Configuration:
  • Topics: types of topics to extract. There are four types available:
    • Entities, with named entities, such as 'James Bond' or 'United Kingdom'.
    • Concepts, with keywords such as 'financial crisis' or 'fish and chips'.
    • Money expressions, such as '$5' or 'ten million euros'.
    • Quantities, such as '0.5%' or 'six months'.
  • API configuration preset: license key and server to use in the API requests. It can be set using one of the presets defined in the plugin Settings or they can be manually defined.
• Advanced: when expert mode is enabled, the following two fields can be configured:
  • Relevance threshold: to filter the topics extracted by their relevance level. By default, it's set to 100, so only the most relevant elements are shown.
  • User dictionary: ID of the user-defined dictionary with additional analyses to take into account in the topic extraction.

The output provided by this recipe will add the following columns to the input dataset:

• topic_person, with the entities and concepts with "Person" as their first level ontology type.
• topic_organization, with the entities and concepts with "Organization" as their first level ontology type.
• topic_location, with the entities and concepts with "Location" as their first level ontology type.
• topic_product, with the entities and concepts with "Product" as their first level ontology type.
• topic_id, with the entities and concepts with "ID" as their first level ontology type.
• topic_event, with the entities and concepts with "Event" as their first level ontology type.
• topic_other, with the entities and concepts with any first level ontology type other than the ones mentioned above.
• topic_quantity, with the money expressions and quantities detected in the text.

Each cell will contain an array with the elements detected in the text for a specific ontology type.

The following example uses the dataset and the user-defined model described in this tutorial. The language is English, the relevance threshold is set to a 100, and all topic types are extracted.