Description Usage Arguments Value See Also
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | 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. |
Data.frame containing parsed content in a tidy fashion.
Check http://www.ibm.com/watson/developercloud/alchemy-language.html for further documentation, and https://alchemy-language-demo.mybluemix.net/?cm_mc_uid=70865809903714586773519&cm_mc_sid_50200000=1468266111 for a web demo.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.