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monkeylearn (version 0.1.0)

monkeylearn_extract: monkeylearn_extract

Description

Access to Monkeylearn extractors modules

Usage

monkeylearn_extract(request, key = monkeylearn_key(quiet = TRUE), extractor_id = "ex_isnnZRbS", verbose = FALSE, params = NULL)

Arguments

request
A vector of characters (each text smaller than 50kB)
key
The API key
extractor_id
The ID of the extractor
verbose
whether to output messages about batch requests
params
parameters for the module as a named list. See the second example.

Value

A data.frame with the results whose attribute is a data.frame "headers" including the number of remaining queries as "x.query.limit.remaining". Both data.frames include a column with the (list of) md5 checksum(s) of the corresponding text(s) computed using the digest digest function.

Details

Find IDs of extractors using https://app.monkeylearn.com/main/explore. Within the free plan, you can make up to 20 requests per minute.

You can use batch to send up to 200 texts to be analyzed within the API (classification or extraction) with each request. So for example, if you need to analyze 6000 tweets, instead of doing 6000 requests to the API, you can use batch to send 30 requests, each request with 200 tweets. The function automatically makes these batch calls and waits if there is a throttle limit error, but you might want to control the process yourself using several calls to the function.

You can check the number of calls you can still make in the API using attr(output, "headers")$x.query.limit.remaining and attr(output, "headers")$x.query.limit.limit.

Examples

Run this code
## Not run: 
# text <- "In the 19th century, the major European powers had gone to great lengths
# to maintain a balance of power throughout Europe, resulting in the existence of
#  a complex network of political and military alliances throughout the continent by 1900.[7]
#   These had started in 1815, with the Holy Alliance between Prussia, Russia, and Austria.
#   Then, in October 1873, German Chancellor Otto von Bismarck negotiated the League of
#    the Three Emperors (German: Dreikaiserbund) between the monarchs of Austria-Hungary,
#     Russia and Germany."
# output <- monkeylearn_extract(request = text)
# output
# # example with parameters
# text <- "A panel of Goldman Sachs employees spent a recent Tuesday night at the
# Columbia University faculty club trying to convince a packed room of potential
# recruits that Wall Street, not Silicon Valley, was the place to be for computer
# scientists.\n\n The Goldman employees knew they had an uphill battle. They were
#  fighting against perceptions of Wall Street as boring and regulation-bound and
#  Silicon Valley as the promised land of flip-flops, beanbag chairs and million-dollar
#   stock options.\n\n Their argument to the room of technologically inclined students
#   was that Wall Street was where they could find far more challenging, diverse and,
#    yes, lucrative jobs working on some of the worlds most difficult technical problems.\n\n
#    Whereas in other opportunities you might be considering, it is working one type of data
#    or one type of application, we deal in hundreds of products in hundreds of markets, with
#     thousands or tens of thousands of clients, every day, millions of times of day worldwide,
#      Afsheen Afshar, a managing director at Goldman Sachs, told the students."
# output <- monkeylearn_extract(text,
#                               extractor_id = "ex_y7BPYzNG",
#                               params = list(max_keywords = 3,
#                                             use_company_names = 1))
# attr(output, "headers")## End(Not run)

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