Are you curious of people's opinions and emotions about your products, brand, or service? With sentiment analysis, you can know their emotions and feelings about your brand. Basically sentiment analysis is a process of decoding the emotions hidden behind people's words and understanding whether feedback is positive, negative or neutral.

That's why to make your brand analysis easier, I'll tell you how to implement sentiment analysis using Apache OpenNLP. It's a robust java-based library for natural level processing. Let's understand it with a coding example:

 

Setting Up the Environment

You must have these tools in place:

• Apache OpenNLP library
• Java Development Kit (JDK)
• Maven or Gradle (for dependency management)

Let's set up the environment:

• Download OpenNLP from the official Apache website.
• Next, add the OpenNLP dependency to your Maven or Gradle project:

<dependency>
 <groupId>org.apache.opennlp</groupId>
 <artifactId>opennlp-tools</artifactId>
  <version>1.9.4</version>
</dependency>

• Now, you've to import necessary OpenNLP classes into your Java code, like SentimentME and SentimentModel.

Preparing the Sentiment Model

Apache OpenNLP needs pre-trained models to process natural language. And you'll need pre-trained model, like en-sentiment.bin, for sentiment analysis. You can download this model from available NLP repositories or can train your own model, in case you don't have one.

To load the model use this code:

InputStream modelStream = new FileInputStream("en-sentiment.bin");
SentimentModel model = new SentimentModel(modelStream);

Implementing Sentiment Analysis

Once you're done loading your model, you can use the following code to perform sentiment analysis using Apache OpenNLP. This will take text input from you, analyse the input, predict it, and then prints the output.

import opennlp.tools.sentiment.SentimentME;
import opennlp.tools.sentiment.SentimentModel;
import java.io.*;

public class SentimentAnalyzer {
    public static void main(String[] args) throws IOException {
        // Load the sentiment model
        InputStream modelStream = new FileInputStream("en-sentiment.bin");
        SentimentModel model = new SentimentModel(modelStream);

        // Initialize the sentiment analysis engine
        SentimentME sentimentME = new SentimentME(model);

        // Provide sample input
        String sentence = "I love this product!";
        double[] outcomes = sentimentME.predict(sentence);

        // Get the predicted sentiment category
        String sentiment = sentimentME.getBestCategory(outcomes);
        System.out.println("Sentiment: " + sentiment);
    }
}

In the above code, SentimentME class initialize your sentiment model and perform predictions. Next I've used the predict() method, it analyzes the sentence and returns an array of options for each sentiment category like positive, negative, neutral. At the end, here's getBestCategory() method, it show the highest possible result.

Testing and Improving Accuracy

After running the above code, now it's time to get reliable predictions. For this, test your model with a variety of sample texts. You must try positive, negative, and neutral sentences/feedbacks to validate the output. For example:

• The service was amazing! Positive
• I'm not satisfied with the experience. Negative
• It was okay, nothing special. Neutral

And in case you didn't get good results, then you can train a custom sentiment model with a labeled dataset. You can also fine-tune models on Apache OpenNLP, doing this your results will get better.

Conclusion

In this blogpost, you've learnt Apache OpenNLP sentiment analysis. I shared example code for setting up the environment and predicting text sentiment. 
Brands that want to track feedback and make data-driven choices may take help from sentiment analysis. OpenNLP lets you try tokenization and named entity recognition. After learning the fundamentals, try sentiment analysis in your projects!