README

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{mscstexta4r}

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.

What’s the Text Analytics REST API?

Per Microsoft’s website, the Text Analytics REST API is a suite of text analytics web services built with Azure Machine Learning that can be used to analyze unstructured text. The API supports the following operations:

Sentiment analysis

The API returns a numeric score between 0 and 1. Scores close to 1 indicate positive sentiment and scores close to 0 indicate negative sentiment. Sentiment score is generated using classification techniques. The input features of the classifier include n-grams, features generated from part-of-speech tags, and word embeddings. English, French, Spanish and Portuguese text are supported.

Topic detection

This API returns the detected topics for a list of submitted text records. A topic is identified with a key phrase, which can be one or more related words. This API requires a minimum of 100 text records to be submitted, but is designed to detect topics across hundreds to thousands of records. The API is designed to work well for short, human-written text such as reviews and user feedback. English is the only language supported at this time.

Language detection

This API returns the detected language and a numeric score between 0 and 1. Scores close to 1 indicate 100% certainty that the identified language is correct. A total of 120 languages are supported.

Extraction of key talking points

This API returns a list of strings denoting the key talking points in the input text. English, German, Spanish, and Japanese text are supported.

Package Installation

To use the {mscstexta4r} R package, you MUST have a valid account with Microsoft Cognitive Services. 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, 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.

You can install the latest stable version of {mscstexta4r} from CRAN as follows:

if ("mscstexta4r" %in% installed.packages()[,"Package"] == FALSE) {
  install.packages("mscstexta4r")
}

if ("mscstexta4r" %in% installed.packages()[,"Package"] == FALSE) {
  if ("devtools" %in% installed.packages()[,"Package"] == FALSE) {
    install.packages("devtools")
  }
  devtools::install_github("philferriere/mscstexta4r")
}

Package Loading and Configuration

After loading {mscstexta4r} with library(), you must call 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.

{
  "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 from two Sys env variables instead:

Error Handling

The MSCS Text Analytics API is a RESTful 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} core functions. Its mechanism may appear a bit daunting at first, but it is well documented. We’ve also included many examples, as you’ll see below.

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() in synchronous mode, in which case, again, it will return only after topic detection has completed.

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 repeatedly afterwards, until results are available.

Package Configuration with Error Handling

library('mscstexta4r')
tryCatch({

  textaInit()

}, error = function(err) {

  geterrmessage()

})

If {mscstexta4r} cannot locate .mscskeys.json nor any of the configuration environment variables, the code above will generate the following output:

[1] "mscstexta4r: could not load config info from Sys env nor from file"

Similarly, textaInit() will fail if {mscstexta4r} cannot find the textanalyticskey key in .mscskeys.json, or fails to parse it correctly, etc. This is why it is so important to use tryCatch() with all {mscstexta4r} functions.

Package API

# Perform sentiment analysis
textaSentiment(
  documents,                  # Input sentences or documents
  languages = rep("en", length(documents))
  # "en"(English, default)|"es"(Spanish)|"fr"(French)|"pt"(Portuguese)
)

# Detect top topics in group of documents
textaDetectTopics(
  documents,                  # At least 100 documents (English only)
  stopWords = NULL,           # Stop word list (optional)
  topicsToExclude = NULL,     # Topics to exclude (optional)
  minDocumentsPerWord = NULL, # Threshold to exclude rare topics (optional)
  maxDocumentsPerWord = NULL, # Threshold to exclude ubiquitous topics (optional)
  resultsPollInterval = 30L,  # Poll interval (in s, default: 30s, use 0L for async)
  resultsTimeout = 1200L,     # Give up timeout (in s, default: 1200s = 20mn)
  verbose = FALSE             # If set to TRUE, print every poll status to stdout
)

# Detect languages used in documents
textaDetectLanguages(
  documents,                      # Input sentences or documents
  numberOfLanguagesToDetect = 1L  # Default: 1L
)

  # Get key talking points in documents
textaKeyPhrases(
  documents,                  # Input sentences or documents
  languages = rep("en", length(documents))
  # "en"(English, default)|"de"(German)|"es"(Spanish)|"fr"(French)|"ja"(Japanese)
)

The functions textaDetectTopics() returns a S3 class 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).

The other functions return S3 class objects of the class texta. The texta object exposes results collected in a single data.frame, the REST API JSON response, and the original HTTP request.

Sample Code

The following code snippets illustrate how to use {mscstexta4r} functions and show what results they return with toy examples. If after reviewing this code there is still confusion regarding how and when to use each function, please refer to the original documentation.

Sentiment Analysis

docsText <- c(
  "Loved the food, service and atmosphere! We'll definitely be back.",
  "Very good food, reasonable prices, excellent service.",
  "It was a great restaurant.",
  "If steak is what you want, this is the place.",
  "The atmosphere is pretty bad but the food is quite good.",
  "The food is quite good but the atmosphere is pretty bad.",
  "The food wasn't very good.",
  "I'm not sure I would come back to this restaurant.",
  "While the food was good the service was a disappointment.",
  "I was very disappointed with both the service and my entree."
)
docsLanguage <- rep("en", length(docsText))

tryCatch({

  # Perform sentiment analysis
  textaSentiment(
    documents = docsText,    # Input sentences or documents
    languages = docsLanguage
    # "en"(English, default)|"es"(Spanish)|"fr"(French)|"pt"(Portuguese)
)

}, error = function(err) {

  # Print error
  geterrmessage()

})
#> texta [https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment]
#> 
#> --------------------------------------
#>              text               score 
#> ------------------------------ -------
#>  Loved the food, service and   0.9847 
#>  atmosphere! We'll definitely         
#>            be back.                   
#> 
#>   Very good food, reasonable   0.9831 
#>   prices, excellent service.          
#> 
#>   It was a great restaurant.   0.9306 
#> 
#>   If steak is what you want,   0.8014 
#>       this is the place.              
#> 
#>  The atmosphere is pretty bad  0.4998 
#>  but the food is quite good.          
#> 
#> The food is quite good but the  0.475 
#>   atmosphere is pretty bad.           
#> 
#>   The food wasn't very good.   0.1877 
#> 
#> I'm not sure I would come back 0.2857 
#>      to this restaurant.              
#> 
#>  While the food was good the   0.08727
#> service was a disappointment.         
#> 
#>  I was very disappointed with  0.01877
#>    both the service and my            
#>            entree.                    
#> --------------------------------------

Topic Detection (synchronous mode)

# Load yelpChReviews100 text reviews
load("./tests/testthat/data/yelpChineseRestaurantReviews100.rda")

tryCatch({

  # Detect top topics
  textaDetectTopics(
    documents = yelpChReviews100, # At least 100 docs/sentences (English only)
    stopWords = NULL,             # Stop word list (optional)
    topicsToExclude = NULL,       # Topics to exclude (optional)
    minDocumentsPerWord = NULL,   # Threshold to exclude rare topics (optional)
    maxDocumentsPerWord = NULL,   # Threshold to exclude ubiquitous topics (optional)
    resultsPollInterval = 30L,    # Poll interval (in s, default: 30s, use 0L for async)
    resultsTimeout = 1200L,       # Give up timeout (in s, default: 1200s = 20mn)
    verbose = FALSE               # If set to TRUE, print every poll status to stdout
  )

}, error = function(err) {

  # Print error
  geterrmessage()

})
#> textatopics [https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/topics?]
#> status: Succeeded
#> operationId: aa3fae26e8aa4a828779ec475e6131da
#> operationType: topics
#> topics (first 20):
#> 
#> -------------------
#>  keyPhrase   score 
#> ----------- -------
#>    soup       19   
#> 
#>    beef       10   
#> 
#>    curry       8   
#> 
#>     egg        7   
#> 
#>   flavor       7   
#> 
#>    pork        7   
#> 
#>    China       6   
#> 
#>    roll        6   
#> 
#>   people       5   
#> 
#>   review       5   
#> 
#>   wontons      5   
#> 
#>    sushi       5   
#> 
#>  delivery      5   
#> 
#>    town        4   
#> 
#>   Phoenix      4   
#> 
#>    rolls       4   
#> 
#>   couple       4   
#> 
#>   tables       4   
#> 
#>   Buffet       4   
#> 
#>    yelp        3   
#> -------------------

Topic Detection (asynchronous mode)

# Load yelpChReviews100 text reviews
load("./tests/testthat/data/yelpChineseRestaurantReviews100.rda")

tryCatch({

  # Detect top topics
  operation <- textaDetectTopics(
    documents = yelpChReviews100, # At least 100 docs/sentences (English only)
    resultsPollInterval = 0L      # Poll interval (in s, default: 30s, use 0L for async)
  )

  # Poll the servers ourselves, until the work completes or until we time out
  resultsPollInterval <- 30L
  resultsTimeout <- 1200L
  startTime <- Sys.time()
  endTime <- startTime + resultsTimeout

  while (Sys.time() <= endTime) {
    sleepTime <- startTime + resultsPollInterval - Sys.time()
    if (sleepTime > 0)
      Sys.sleep(sleepTime)
    startTime <- Sys.time()

    # Poll for results
    topics <- textaDetectTopicsStatus(operation)
    if (topics$status != "NotStarted" && topics$status != "Running")
      break;
  }

  topics

}, error = function(err) {

  # Print error
  geterrmessage()

})
# Same results as in synchronous mode

Language Detection

docsText = c(
  "The Louvre or the Louvre Museum is the world's largest museum.",
  "Le musee du Louvre est un musee d'art et d'antiquites situe au centre de Paris.",
  "El Museo del Louvre es el museo nacional de Francia.",
  "Il Museo del Louvre a Parigi, in Francia, e uno dei piu celebri musei del mondo.",
  "Der Louvre ist ein Museum in Paris."
)

tryCatch({

  # Detect languages
  textaDetectLanguages(
    documents = docsText,           # Input sentences or documents
    numberOfLanguagesToDetect = 1L  # Number of languages to detect
  )

}, error = function(err) {

  # Print error
  geterrmessage()

})
#> texta [https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/languages?numberOfLanguagesToDetect=1]
#> 
#> -----------------------------------------------------------
#>             text               name    iso6391Name   score 
#> ----------------------------- ------- ------------- -------
#>   The Louvre or the Louvre    English      en          1   
#> Museum is the world's largest                              
#>            museum.                                         
#> 
#>   Le musee du Louvre est un   French       fr          1   
#>  musee d'art et d'antiquites                               
#>   situe au centre de Paris.                                
#> 
#>   El Museo del Louvre es el   Spanish      es          1   
#>  museo nacional de Francia.                                
#> 
#> Il Museo del Louvre a Parigi, Italian      it          1   
#>   in Francia, e uno dei piu                                
#>   celebri musei del mondo.                                 
#> 
#> Der Louvre ist ein Museum in  German       de          1   
#>            Paris.                                          
#> -----------------------------------------------------------

Key Talking Points Extraction

docsText <- c(
  "Loved the food, service and atmosphere! We'll definitely be back.",
  "Very good food, reasonable prices, excellent service.",
  "It was a great restaurant.",
  "If steak is what you want, this is the place.",
  "The atmosphere is pretty bad but the food is quite good.",
  "The food is quite good but the atmosphere is pretty bad.",
  "I'm not sure I would come back to this restaurant.",
  "The food wasn't very good.",
  "While the food was good the service was a disappointment.",
  "I was very disappointed with both the service and my entree."
)
docsLanguage <- rep("en", length(docsText))

tryCatch({

  # Get key talking points in documents
  textaKeyPhrases(
    documents = docsText,    # Input sentences or documents
    languages = docsLanguage
    # "en"(English, default)|"de"(German)|"es"(Spanish)|"fr"(French)|"ja"(Japanese)
  )

}, error = function(err) {

  # Print error
  geterrmessage()

})
#> texta [https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/keyPhrases]
#> 
#> -----------------------------------------------------------
#>              text                       keyPhrases         
#> ------------------------------ ----------------------------
#>  Loved the food, service and    atmosphere, food, service  
#>  atmosphere! We'll definitely                              
#>            be back.                                        
#> 
#>   Very good food, reasonable   reasonable prices, good food
#>   prices, excellent service.                               
#> 
#>   It was a great restaurant.         great restaurant      
#> 
#>   If steak is what you want,           steak, place        
#>       this is the place.                                   
#> 
#>  The atmosphere is pretty bad        atmosphere, food      
#>  but the food is quite good.                               
#> 
#> The food is quite good but the       food, atmosphere      
#>   atmosphere is pretty bad.                                
#> 
#> I'm not sure I would come back          restaurant         
#>      to this restaurant.                                   
#> 
#>   The food wasn't very good.               food            
#> 
#>  While the food was good the          service, food        
#> service was a disappointment.                              
#> 
#>  I was very disappointed with        service, entree       
#>    both the service and my                                 
#>            entree.                                         
#> -----------------------------------------------------------

Credits

For great introductions to the underlying REST API, please refer to this and that link.

Related Microsoft Cognitive Services Packages

{mscsweblm4r}, a R Client for the Microsoft Cognitive Services Web Language Model REST API, is also available on CRAN