SentimentAnalyser - Scoped

The SentimentAnalyser script include provides methods to perform sentiment analysis on a string value.

You should use this script include in a script that is treated as an admin-executing script. For example, use the Sentiment Analysis script includein a 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.

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.

표 1. Parameters

Name	Type	Description
configGR	GlideRecord	GlideRecord object of a connector configuration.

var sa = new sn_nlp_sentiment.SentimentAnalyser(configGR);

SentimentAnalyser - analyze(String inputText)

Performs sentiment analysis on the specified text.

표 2. Parameters

Name	Type	Description
inputText	String	Text on which sentiment analysis should be performed.

표 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.

        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 - analyzeMultiple(Array inputTextArray)

Performs sentiment analysis on an array of strings.

표 4. Parameters

Name	Type	Description
inputTextArray	Array	Array of text (string) on which to perform sentiment analysis.

표 5. 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.

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.

표 6. 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.

표 7. 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.

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 - analyzeWithLanguage(String inputText, String language)

Performs sentiment analysis on a specified text and language.

표 8. 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.

표 9. 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.

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 - getConnectorByName(String connectorName)

Returns the GlideRecord of the specified connector configuration.

표 10. Parameters

Name	Type	Description
connectorName	String	Name of the connector configuration.

표 11. Returns

Type	Description
GlideRecord	GlideRecord of the specified connector configuration.

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.

표 12. Parameters

Name	Type	Description
None

표 13. Returns

Type	Description
GlideRecord	GlideRecord of the default connector configuration.

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