text_sentiment: Process text with IBM Alchemy Language algorithms
In ColumbusCollaboratory/cognizer: Access to Cognitive APIs

Description Usage Arguments Value See Also

View source: R/text_cognizers.R

text_sentiment: Takes a vector of text and sends to Watson services for various analyses. Requires basic authentication using api key.

text_keywords: Keywords analysis extracts keywords from text, and can optionally provide their sentiment and/or associated knowledge graph.

text_emotion: Emotion analysis of text infers scores for 7 basic emotions.

text_language: Language detection infers language of the provided text. Works best with at least 100 words.

text_entity: Entity analysis extracts names of people, products, places from the provided text. Additional arguments can provide sentiment, knowledge graphs and quotations related to inferred entities.

text_concept: Concept analysis infers categories based on the text, but that are not necessarily in the text. Additional arguments can provide sentiment and/or knowledge graphs related to inferred concepts.

text_relations: Relation analysis infers associations among entities.

text_taxonomy: Taxonomy analysis infers hierarchical relations among entities upto 5 levels deep.

text_sentiment(text, api_key, output_mode = "json", show_source = 0,
  keep_data = "true", callback = NULL)

text_keywords(text, api_key, output_mode = "json", show_source = 0,
  keep_data = "true", callback = NULL, max_retrieve = 50,
  knowledge_graph = 0, sentiment = 0)

text_emotion(text, api_key, output_mode = "json", show_source = 0,
  keep_data = "true", callback = NULL)

text_language(text, api_key, output_mode = "json", show_source = 0,
  keep_data = "true", callback = NULL)

text_entity(text, api_key, output_mode = "json", show_source = 0,
  keep_data = "true", callback = NULL, max_retrieve = 50,
  knowledge_graph = 0, sentiment = 0, model = "ie-en-news",
  coreference = 1, disambiguate = 1, linked_data = 1, quotations = 0,
  structured_entity = 1)

text_concept(text, api_key, output_mode = "json", show_source = 0,
  keep_data = "true", callback = NULL, max_retrieve = 8,
  knowledge_graph = 0, linked_data = 1)

text_relations(text, api_key, output_mode = "json", show_source = 0,
  keep_data = "true", callback = NULL, model = "ie-en-news")

text_taxonomy(text, api_key, output_mode = "json", show_source = 0,
  keep_data = "true", callback = NULL, max_retrieve = 50,
  knowledge_graph = 0, sentiment = 0, model = "ie-en-news",
  coreference = 1, disambiguate = 1, linked_data = 1, quotations = 0,
  structured_entity = 1)

`text`	Character vector containing strings to be processed.
`api_key`	Character scalar containing api key obtained from Watson services.
`output_mode`	Character scalar specifying returned data structure. Alternative is xml.
`show_source`	Intenger scalar specifying whether to send text string back or not.
`keep_data`	Character scalar specifying whether to share your data with Watson services for the purpose of training their models.
`callback`	Function that can be applied to responses to examine http status, headers, and content, to debug or to write a custom parser for content. The default callback parses content into a data.frame while dropping other response values to make the output easily passable to tidyverse packages like dplyr or ggplot2. For further details or debugging one can pass a fail or a more compicated function.
`max_retrieve`	Integer scalar fixing the number of keywords to extract from text.
`knowledge_graph`	Integer scalar indicating whether to grab a knowledge graph associated with keywords. This is an additional transaction.
`sentiment`	Integer scalar indicating whether to infer sentiment of keywords, expressed as category and number. This is an additional transaction.
`model`	Character scalar specifying one of three models which will extract entities. Alternatives are 'ie-es-news', 'ie-ar-news' or a custom model.
`coreference`	Integer scalar specifying whether to resolve coreferences into detected entities.
`disambiguate`	Integer scalar specifying whether to disambiguate detected entities.
`linked_data`	Integer scalar specifying whether to include links for related data.
`quotations`	Integer scalar specifying whether to include quotes related to detected entities.
`structured_entity`	Integer scalar specifying whether to extract structured entities, such as Quantity, EmailAddress, TwitterHandle, Hashtag, and IPAddress.