mscstexta4r: R Client for the Microsoft Cognitive Services Text Analytics...

In philferriere/mscstexta4r: R Client for the Microsoft Cognitive Services Text Analytics REST API

Description

mscstexta4r is a client/wrapper/interface for the Microsoft Cognitive Services (MSCS) Text Analytics (Text Analytics) REST API. To use this package, you MUST have a valid account with https://www.microsoft.com/cognitive-services. Once you have an account, Microsoft will provide you with a (free) API key you can use with this package.

The MSCS Text Analytics REST API

Microsoft Cognitive Services – formerly known as Project Oxford – are a set of APIs, SDKs and services that developers can use to add AI features to their apps. Those features include emotion and video detection; facial, speech and vision recognition; as well as speech and NLP.

The Text Analytics REST API provides tools for NLP and is documented at https://www.microsoft.com/cognitive-services/en-us/text-analytics/documentation. This API supports the following operations:

• Sentiment analysis - Is a sentence or document generally positive or negative?

• Topic detection - What's being discussed across a list of documents/reviews/articles?

• Language detection - What language is a document written in?

• Key talking points extraction - What's being discussed in a single document?

mscstexta4r Functions

The following mscstexta4r core functions are used to wrap the MSCS Text Analytics REST API:

• Sentiment analysis - textaSentiment function

• Topic detection - textaDetectTopics and textaDetectTopicsStatus functions

• Language detection - textaDetectLanguages function

• Extraction of key talking points - textaKeyPhrases function

The textaInit configuration function is used to set the REST API URL and the private API key. It needs to be called only once, after package load, or the core functions will not work properly.

Prerequisites

To use the mscstexta4r R package, you MUST have a valid account with Microsoft Cognitive Services (see https://www.microsoft.com/cognitive-services/en-us/pricing for details). Once you have an account, Microsoft will provide you with an API key listed under your subscriptions. After you've configured mscstexta4r with your API key (as explained in the next section), you will be able to call the Text Analytics REST API from R, up to your maximum number of transactions per month and per minute.

Package Loading and Configuration

After loading the mscstexta4r package with the library() function, you must call the textaInit before you can call any of the core mscstexta4r functions.

The textaInit configuration function will first check to see if the variable MSCS_TEXTANALYTICS_CONFIG_FILE exists in the system environment. If it does, the package will use that as the path to the configuration file.

If MSCS_TEXTANALYTICS_CONFIG_FILE doesn't exist, it will look for the file .mscskeys.json in the current user's home directory (that's ~/.mscskeys.json on Linux, and something like C:/Users/Phil/Documents/.mscskeys.json on Windows). If the file is found, the package will load the API key and URL from it.

If using a file, please make sure it has the following structure:

{
  "textanalyticsurl": "https://westus.api.cognitive.microsoft.com/texta/analytics/v2.0/",
  "textanalyticskey": "...MSCS Text Analytics API key goes here..."
}

If no configuration file is found, textaInit will attempt to pick up its configuration information from two Sys env variables instead:

MSCS_TEXTANALYTICS_URL - the URL for the Text Analytics REST API.

MSCS_TEXTANALYTICS_KEY - your personal Text Analytics REST API key.

Synchronous vs Asynchronous Execution

All but ONE core text analytics functions execute exclusively in synchronous mode: textaDetectTopics is the only function that can be executed either synchronously or asynchronously. Why? Because topic detection is typically a "batch" operation meant to be performed on thousands of related documents (product reviews, research articles, etc.).

What's the difference?

When textaDetectTopics executes synchronously, you must wait for it to finish before you can move on to the next task. When textaDetectTopics executes asynchronously, you can move on to something else before topic detection has completed. In the latter case, you will need to call textaDetectTopicsStatus periodically yourself until the Microsoft Cognitive Services server complete topic detection and results become available.

When to run which mode?

If you're performing topic detection in batch mode (from an R script), we recommend using the textaDetectTopics function in synchronous mode, in which case it will return only after topic detection has completed.

IMPORTANT NOTE: If you're calling textaDetectTopics in synchronous mode within the R console REPL (interactive mode), it will appear as if the console has hanged. This is EXPECTED. The function hasn't crashed. It is simply in "sleep mode", activating itself periodically and then going back to sleep, until the results have become available. In sleep mode, even though it appears "stuck", textaDetectTopics doesn't use any CPU resources. While the function is operating in sleep mode, you WILL NOT be able to use the console before the function completes. If you need to operate the console while topic detection is being performed by the Microsoft Cognitive services servers, you should call textaDetectTopics in asynchronous mode and then call textaDetectTopicsStatus yourself repeteadly afterwards, until results are available.

S3 Objects of the Classes texta and textatopics

The sentiment analysis, language detection, and key talking points extraction functions of the mscstexta4r package return S3 objects of the class texta. The texta object exposes results collected in a single dataframe, the REST API JSON response, and the original HTTP request.

The functions textaDetectTopics returns a S3 object of the class textatopics. The textatopics object exposes formatted results using several dataframes (documents and their IDs, topics and their IDs, which topics are assigned to which documents), the REST API JSON response (should you care), and the HTTP request (mostly for debugging purposes).'

Error Handling

The MSCS Text Analytics API is a REST API. HTTP requests over a network and the Internet can fail. Because of congestion, because the web site is down for maintenance, because of firewall configuration issues, etc. There are many possible points of failure.

The API can also fail if you've exhausted your call volume quota or are exceeding the API calls rate limit. Unfortunately, MSCS does not expose an API you can query to check if you're about to exceed your quota for instance. The only way you'll know for sure is by looking at the error code returned after an API call has failed.

To help with error handling, we recommend the systematic use of tryCatch() when calling mscstexta4r's core functions. Its mechanism may appear a bit daunting at first, but it is well documented at http://www.inside-r.org/r-doc/base/signalCondition. We use it in many of the code examples.

Author(s)

Phil Ferriere [e-mail]

philferriere/mscstexta4r documentation built on May 25, 2019, 5:03 a.m.