Sentiment analysis is a technique used in natural language processing to determine the sentiment (positive, negative, or neutral) of a given piece of text. It has numerous applications, such as analyzing customer feedback, monitoring social media sentiment, and improving product reviews.

In this blog post, we will explore how to implement sentiment analysis using Nashorn, a JavaScript engine for the Java Virtual Machine, and Apache Lucene, a powerful text search and analysis library.

Table of Contents

• What is sentiment analysis?
• Setting up Nashorn and Apache Lucene
• Building a sentiment analysis model
• Performing sentiment analysis
• Conclusion

What is sentiment analysis?

Sentiment analysis, also known as opinion mining, is the process of determining the sentiment expressed in a piece of text. It involves analyzing the text to understand whether the sentiment is positive, negative, or neutral. This analysis can be done at various levels, such as document level, sentence level, or even aspect level.

Setting up Nashorn and Apache Lucene

To get started, you need to set up Nashorn and Apache Lucene in your Java project. You can add the following dependencies to your pom.xml file:

<dependency>
    <groupId>org.apache.lucene</groupId>
    <artifactId>lucene-core</artifactId>
    <version>8.6.2</version>
</dependency>
<dependency>
    <groupId>jdk.nashorn</groupId>
    <artifactId>nashorn-core</artifactId>
    <version>15.1</version>
</dependency>

Once the dependencies are added, you can import the required classes in your Java code.

import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.standard.StandardAnalyzer;
import org.apache.lucene.document.Document;
import org.apache.lucene.document.Field;
import org.apache.lucene.document.StringField;
import org.apache.lucene.document.TextField;
import org.apache.lucene.index.IndexWriter;
import org.apache.lucene.index.IndexWriterConfig;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.Query;
import org.apache.lucene.search.ScoreDoc;
import org.apache.lucene.search.TopDocs;
import org.apache.lucene.store.Directory;
import org.apache.lucene.store.RAMDirectory;
import org.apache.lucene.util.QueryBuilder;

import jdk.nashorn.api.scripting.NashornScriptEngine;
import jdk.nashorn.api.scripting.ScriptObjectMirror;
import jdk.nashorn.api.scripting.ScriptUtils;
import jdk.nashorn.api.scripting.ScriptUtils.ScriptEngineOptions;

Building a sentiment analysis model

To perform sentiment analysis, we need a pre-trained model that can classify text as positive, negative, or neutral. In this example, we will use a simple model represented as a JavaScript function.

var analyzeSentiment = function(text) {
    // Perform sentiment analysis logic here
    return "positive"; // Placeholder, replace with actual analysis
};

Note that in a real-world scenario, you would typically use machine learning techniques to build a more accurate sentiment analysis model.

Performing sentiment analysis

Now that we have set up Nashorn and have a basic sentiment analysis model, let’s put it to use. We will create an index of text documents using Apache Lucene and then analyze the sentiment of a given text using the JavaScript function.

// Set up a Lucene index
Directory directory = new RAMDirectory();
Analyzer analyzer = new StandardAnalyzer();
IndexWriterConfig config = new IndexWriterConfig(analyzer);
IndexWriter indexWriter = new IndexWriter(directory, config);

// Add documents to the index
Document document = new Document();
document.add(new StringField("id", "1", Field.Store.YES));
document.add(new TextField("text", "I love this product!", Field.Store.YES));
indexWriter.addDocument(document);

// Commit changes and close the index writer
indexWriter.commit();
indexWriter.close();

// Perform sentiment analysis
String script = "analyzeSentiment";
NashornScriptEngine scriptEngine = (NashornScriptEngine) ScriptUtils.getScriptEngine(ScriptEngineOptions.STANDARD);
ScriptObjectMirror sentimentAnalysisFunction = (ScriptObjectMirror) scriptEngine.eval(script);

IndexSearcher searcher = new IndexSearcher(DirectoryReader.open(directory));
QueryBuilder queryBuilder = new QueryBuilder(analyzer);
Query query = queryBuilder.createPhraseQuery("text", "I love this product!");
TopDocs topDocs = searcher.search(query, 10);
ScoreDoc[] scoreDocs = topDocs.scoreDocs;

for (ScoreDoc scoreDoc : scoreDocs) {
    Document retrievedDoc = searcher.doc(scoreDoc.doc);
    String text = retrievedDoc.get("text");
    String sentiment = sentimentAnalysisFunction.call(null, text).toString();

    System.out.println("Sentiment of '" + text + "': " + sentiment);
}

In the above code, we create a Lucene index with a single document containing some positive text. We then perform a search and pass each retrieved document’s text to the sentiment analysis function. Finally, we print the sentiment result for each text.

Conclusion

Sentiment analysis is a powerful technique for understanding the sentiment expressed in textual data. By combining Nashorn, a JavaScript engine for the JVM, and Apache Lucene, a robust text search and analysis library, we can easily implement sentiment analysis in Java. Keep in mind that this example uses a basic sentiment analysis model, but in real-world applications, more advanced techniques would be required for accurate sentiment analysis results.

#MachineLearning #NaturalLanguageProcessing