Sentiment analysis

Sentiment analysis is a type of document analysis that determines how positive or negative the tone of the text is.

Sentiment analysis also performs knowledge linking: Knowledge Graph information and open data—Wikidata, DBpedia and GeoNames references—are returned for text items that express sentiment given they correspond to syncons of the expert.ai Knowledge Graph. In the case of actual places, geographic coordinates are also provided.

The Studio LDA API carrying out sentiment analysis have the following endpoint:

/api/analyze

In the reference section of this manual you will find all the information you need to perform sentiment analysis, specifically:

• The format of the request.
• The format of the output.

Here is an example of performing sentiment analysis on a short English text:

Python

This example is based on the Python client you can find on GitHub.

The client gets user credentials from two environment variables:

EAI_USERNAME
EAI_PASSWORD

Set those variables with you account credentials before running the sample program below.

The program prints the overall sentiment.

from expertai.nlapi.edge.client import ExpertAiClient
client = ExpertAiClient()

text = "Michael Jordan was one of the best basketball players of all time. Scoring was Jordan's stand-out skill, but he still holds a defensive NBA record, with eight steals in a half." 

output = client.sentiment(text)

# Output overall sentiment

print("Output overall sentiment:")

print(output.sentiment.overall)

Java

This example is based on the Java client you can find on GitHub.

The client gets user credentials from two environment variables:

EAI_USERNAME
EAI_PASSWORD

Set those variables with you account credentials before running the sample program below.

The program prints the JSON response and the overall sentiment.

import ai.expert.nlapi.security.Authentication;
import ai.expert.nlapi.security.Authenticator;
import ai.expert.nlapi.security.BasicAuthenticator;
import ai.expert.nlapi.security.DefaultCredentialsProvider;
import ai.expert.nlapi.v2.API;
import ai.expert.nlapi.v2.edge.Analyzer;
import ai.expert.nlapi.v2.edge.AnalyzerConfig;
import ai.expert.nlapi.v2.message.AnalyzeResponse;
import ai.expert.nlapi.v2.model.AnalyzeDocument;

public class Main {

    public static Authentication createAuthentication() throws Exception {
        DefaultCredentialsProvider credentialsProvider = new DefaultCredentialsProvider();
        Authenticator authenticator = new BasicAuthenticator(credentialsProvider);
        return new Authentication(authenticator);
    }

    public static Analyzer createAnalyzer() throws Exception {
        return new Analyzer(AnalyzerConfig.builder()
                .withVersion(API.Versions.V2)
                .withHost(API.DEFAULT_EDGE_HOST)
                .withAuthentication(createAuthentication())
                .build());
    }

    public static void main(String[] args) {
        try {
            String text = "Michael Jordan was one of the best basketball players of all time. Scoring was Jordan's stand-out skill, but he still holds a defensive NBA record, with eight steals in a half.";

            Analyzer analyzer = createAnalyzer();

            AnalyzeResponse sentiment = analyzer.sentiment(text);


            // Output JSON representation

            System.out.println("JSON representation:");
            sentiment.prettyPrint();


            // Overall sentiment.

            System.out.println("Overall sentiment:");
            AnalyzeDocument data = sentiment.getData();
            System.out.println(data.getSentiment().getOverall());
        }
        catch(Exception ex) {
            ex.printStackTrace();
        }
    }
}