The SentimentAnalyser API performs sentiment analysis on a string value.

You should use this API should in a script that is treated as an admin-executing script. For example, we should use the Sentiment Analysis API in Script Action or Scheduled Job.

To use this class in a scoped application, use the sn_nlp_sentiment namespace identifier. The Sentiment Analysis plugin (com.snc.sentiment_analysis) must be enabled to access the SentimentAnalyser API.

SentimentAnalyser - SentimentAnalyser()

Creates an instance of the SentimentAnalyser class with the default connector configuration that is used for sentiment analysis.

Example

var sa = new sn_nlp_sentiment.SentimentAnalyser();

SentimentAnalyser - SentimentAnalyser(GlideRecord configGR)

Creates an instance of the SentimentAnalyser class with the specified connector configuration that is used for sentiment analysis.

Table 1. Parameters
Name Type Description
configGR GlideRecord GlideRecord object of a connector configuration.

Example

var sa = new sn_nlp_sentiment.SentimentAnalyser(configGR);

SentimentAnalyser - analyze(String inputText)

Performs sentiment analysis on the specified text.

Table 2. Parameters
Name Type Description
inputText String Text on which sentiment analysis should be performed.
Table 3. Returns
Type Description
JSON object Result of the sentiment analysis specifying the status, score, normalised score, sys_id of the relevant connector configuration, and error message.

Example


        var sa = new sn_nlp_sentiment.SentimentAnalyser();
        var result = sa.analyze ("Example string");

Output:

{"status": "Success", "score": "0.7", "normalizedScore": "0.7", "connectorConfig": "10932aa773101300734e234ffff6a777", "errorMessage":""}

SentimentAnalyser - analyzeWithLanguage(String inputText, String language)

Performs sentiment analysis on a specified text and language.

Table 4. Parameters
Name Type Description
inputText String Text on which to perform sentiment analysis.
language String Language for the input text. This can vary for different sentiment services.
Table 5. Returns
Type Description
JSON object Result of the sentiment analysis specifying the status, score, normalized score, sys_id of the relevant connector configuration, and error message.

Example

var sa = new sn_nlp_sentiment.SentimentAnalyser();
var result = sa.analyze ("Example string", "en");

Output:

{"status": "Success", "score": "0.7", "normalizedScore": "0.7", "connectorConfig": "10932aa773101300734e234ffff6a777", errorMessage":""}

SentimentAnalyser - analyzeMultiple(Array inputTextArray)

Performs sentiment analysis on an array of strings.

Table 6. Parameters
Name Type Description
inputTextArray Array Array of text (string) on which to perform sentiment analysis.
Table 7. Returns
Type Description
JSON Array An array that gives the result of the sentiment analysis performed on multiple texts specifying the status, score, normalized score, sys_id of the relevant connector configuration, and error message.

Example

var sa = new sn_nlp_sentiment.SentimentAnalyser();
var result = sa.analyzeMultiple (["Example string1","Example string2"]);

Output:

[{"text": "I am happy","result": {Success", "score": "0.7", "normalizedScore": "0.7", "connectorConfig": "10932aa773101300734e234ffff6a777", "errorMessage":""}},{"text": "I am not happy","result": {Success", "score": "-0.7", "normalizedScore": "-0.7", "connectorConfig": "10932aa773101300734e234ffff6a777", "errorMessage":""}}]

SentimentAnalyser - analyzeMultipleWithLanguage(Array inputTextArray, String language)

Performs sentiment analysis on an array of strings in the specified language.

Table 8. Parameters
Name Type Description
inputTextArray Array Array of text (string) on which to perform sentiment analysis.
language String Language for the input text. This can very for different sentiment services.
Table 9. Returns
Type Description
JSON Array An array with the result of the sentiment analysis performed on multiple texts of the mentioned language, specifying the status, score, normalized score, sys_id of the relevant connector configuration, and error message.

Example

var sa = new sn_nlp_sentiment.SentimentAnalyser();
var result = sa.analyzeMultipleWithLanguage (["Example string1","Example string2"], "en");

Output:

[{"text": "I am happy","result": {Success", "score": "0.7", "normalizedScore": "0.7", "connectorConfig": "10932aa773101300734e234ffff6a777", "errorMessage":""}},{"text": "I am not happy","result": {Success", "score": "-0.7", "normalizedScore": "-0.7", "connectorConfig": "10932aa773101300734e234ffff6a777", "errorMessage":""}}]

SentimentAnalyser - getConnectorByName(String connectorName)

Returns the GlideRecord of the specified connector configuration.

Table 10. Parameters
Name Type Description
connectorName String Name of the connector configuration.
Table 11. Returns
Type Description
GlideRecord GlideRecord of the specified connector configuration.

Example

var sa = new sn_nlp_sentiment.SentimentAnalyser();
var connector = sa.getConnectorByName("xxx");

Output:

GlideRecord object of the connector configuration with name "xxx", null if no connector is named as "xxx".

SentimentAnalyser - getDefaultConnector()

Returns the GlideRecord of the default connector configuration.

Table 12. Parameters
Name Type Description
None
Table 13. Returns
Type Description
GlideRecord GlideRecord of the default connector configuration.

Example

var sa = new sn_nlp_sentiment.SentimentAnalyser();
var defaultConnector = sa.getDefaultConnector();