Actions

../_images/action_analysis_icon.png

The Action Analysis widget provides a tool to support basic narrative analysis for actions in stories. It is part of the Orange3-Story-Navigator add-on for the Orange data mining software package. The widget highlights present and past tense actions, calculate their frequency, and identify actors associated with those actions, within in a textual story written in the Dutch language.

Main Features

  • Analyses actions in stories based on part of speech (POS) tagging and verb tense.

  • Allows selection of entity type to highlight (POS or NER).

  • Provides options to filter and highlight specific parts of speech.

  • Supports copying of analysis results to clipboard.

  • Allows for customization of POS checkboxes for each POS type.

Inputs

  • Story elements: The action widget always requires story elements from the elements widget. The widget will not work without this input.

  • Stories: A dataset of one or more textual story documents in Dutch.

  • Token categories (optional): a data table specifying one or more classification schemes of tokens or words. The table should consist of at least two columns. The first column is a list of words or tokens. All subsequent columns should contain strings which represent user-defined category labels for the corresponding word or token in the first column.

Outputs

  • Action stats: A data table with four columns.

    • storyid is the first column, matching with a particular story from the corpus

    • segment_id represents the amount of segments the story has been divided into (see the elements widget)

    • story navigator tag describes the verb tense, indicating the time at which an action takes place (i.e., past, present, or future)

    • wordcol describes the frequency of a verb tense per story id.

  • Custom tag stats (optional): A data table with five columns.

    • storyid, matching with a particular story from the corpus

    • segment_id represents the amount of segments the story has been divided into

    • category further sub-categorizes all the verbs in a story, with the type of sub-category depending on the column ‘classification’. The specific (sub)-categories can be manually specified, depending on the research interests, and is input for the elements widget. Note that a verb can be part of more than 1 category, depending on the context and quality of the verb.

    • freq describes the verb-frequency within each subcategory (i.e., column category), per story id.

    • classification is the higher-order category, as manually specified.

  • Action table: A data table with three columns.

    • action specifies all the verbs which occured accross the corpus. Duplicate verbs occur because the action table accounts for different type of entities associated with the action.

    • entities specifies all the actors associated with the action from the action column, accross the entire corpus.

    • entities_type further specifies the association between action and entity, based on the entity’s morphological property (e.g., singular proper noun, noun that is singular and non-proper, etc.).

Example usage:

../_images/sn_action_analysis_example.png