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Harnessing Sentiment Analysis with Java: A Step-by-Step Guide using NLP

Sentiment analysis, a powerful application of Natural Language Processing (NLP), allows developers to gain insights into the emotions expressed in textual data. In this article, we'll explore how to perform sentiment analysis in Java using the Stanford NLP library. We'll guide you through setting up a Maven project, compiling the code, and running it with practical examples.


Get IntelliJ Idea from https://www.jetbrains.com/idea/download. 
You can skip this and use any other IDE as well. The steps for setting up a maven project will be different for each IDE.

Step 1: Create a New Project

  • Open IntelliJ IDEA and click on "Create New Project." 
  • Choose "Maven" as the project type and click "Next." 
  • Set the "GroupId" to com.simplestcodings and "ArtifactId" to SentimentAnalysis. 
  • Click "Next" and then "Finish." 

Step 2: Add Dependencies

Open the pom.xml file in the editor and replace the content with this section:

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">





Step 3: Create Project Structure

  • Right-click on the src directory in your project and choose "New" -> "Package." 
  • Name it com.simplestcodings. 
  • Inside the com.simplestcodings package, create a new Java class named SentimentAnalysis. 

Step 4: Configure Properties

Right-click on the src directory and create a new directory named resources. Inside the resources directory, create a file named application.properties with the following content:
annotators = tokenize, ssplit, parse, sentiment

Step 5: Write Code

Replace the contents of SentimentAnalysis.java with the code provided below

package com.simplestcodings;

import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations;
import edu.stanford.nlp.util.CoreMap;
import lombok.extern.slf4j.Slf4j;

public class SentimentAnalysis {

    public static void main(String[] args) {
        // Initialize the Stanford NLP pipeline
        StanfordCoreNLP pipeline = new StanfordCoreNLP("application.properties");

        // Sample text for sentiment analysis
        String text = "Simplest codings is the best place to learn and grow. I am glad to be here.";

        // Perform sentiment analysis
        String sentiment = getSentiment(text, pipeline);

        // Display the result
        log.info("Text: {}, Sentiment: {}",text, sentiment);

        // Another Sample text for sentiment analysis
        text = "I hate this place. I am not coming back here again. I am very disappointed.";

        // Perform sentiment analysis
        sentiment = getSentiment(text, pipeline);

        // Display the result
        log.info("Text: {}, Sentiment: {}",text, sentiment);

    private static String getSentiment(String text, StanfordCoreNLP pipeline) {
        // Create an Annotation object with the input text
        Annotation annotation = new Annotation(text);

        // Run all the NLP annotators on the text

        // Extract the sentiment from the annotation
        CoreMap sentence = annotation.get(CoreAnnotations.SentencesAnnotation.class).get(0);
        String sentiment = sentence.get(SentimentCoreAnnotations.SentimentClass.class);

        return sentiment;

Step 6: Run the Code

Right-click on the SentimentAnalysis class and select "Run SentimentAnalysis.main()". Observe the output in the Run console, which should display the sentiment of the provided text.

Harnessing Sentiment Analysis with Java: A Step-by-Step Guide using NLP

You have successfully set up and run a sentiment analysis Java project using the Stanford NLP library in IntelliJ IDEA. Feel free to explore more examples and experiment with different texts to gain insights into the sentiment analysis capabilities of Java and NLP. Happy coding!