Add Sentiment Analysis lab

SentimentAnalysisLab/build.gradle

@@ -0,0 +1,33 @@
+import org.springframework.boot.gradle.plugin.SpringBootPlugin
+
+apply plugin: 'java'
+apply plugin: 'org.springframework.boot'
+apply plugin: 'io.spring.dependency-management'
+
+description = "Sentiment Analysis Lab"
+
+java {
+    sourceCompatibility = JavaVersion.VERSION_21
+    targetCompatibility = JavaVersion.VERSION_21
+}
+
+repositories {
+    mavenCentral()
+}
+
+dependencyManagement {
+    imports {
+        mavenBom SpringBootPlugin.BOM_COORDINATES
+    }
+}
+
+dependencies {
+    implementation 'org.springframework.boot:spring-boot-starter-web'
+
+    implementation 'com.opencsv:opencsv:5.7.1'
+    implementation 'edu.stanford.nlp:stanford-corenlp:4.5.9:models'
+    implementation 'edu.stanford.nlp:stanford-corenlp:4.5.9'
+
+    runtimeOnly 'org.springframework.boot:spring-boot-devtools'
+    testImplementation 'org.springframework.boot:spring-boot-starter-test'
+}

SentimentAnalysisLab/src/main/java/com/example/senti/ProductReviewAnalyzer.java

@@ -0,0 +1,120 @@
+package com.example.senti;
+
+import edu.stanford.nlp.ling.CoreAnnotations;
+import edu.stanford.nlp.neural.rnn.RNNCoreAnnotations;
+import edu.stanford.nlp.pipeline.*;
+import edu.stanford.nlp.sentiment.SentimentCoreAnnotations;
+import edu.stanford.nlp.util.CoreMap;
+import com.opencsv.CSVReader;
+import com.opencsv.exceptions.CsvException;
+import org.springframework.core.io.ClassPathResource;
+
+
+import java.io.FileReader;
+import java.io.IOException;
+import java.io.InputStreamReader;
+import java.util.*;
+
+public class ProductReviewAnalyzer {
+
+    private StanfordCoreNLP pipeline;
+
+    public static void main(String[] args) {
+        ProductReviewAnalyzer analyzer = new ProductReviewAnalyzer();
+        analyzer.initialize();
+        
+        try {
+            // Load reviews from CSV
+            List<String[]> reviews = analyzer.loadReviews("/product_reviews.csv");
+            Map<String, Integer> sentimentDistribution = new LinkedHashMap<>();
+            sentimentDistribution.put("Very Positive", 0);
+            sentimentDistribution.put("Positive", 0);
+            sentimentDistribution.put("Neutral", 0);
+            sentimentDistribution.put("Negative", 0);
+            sentimentDistribution.put("Very Negative", 0);
+            
+            System.out.println("=== Review Sentiment Analysis ===");
+            for (String[] review : reviews) {
+                if (review[0].equals("review_id")) continue; // Skip header
+                
+                String sentiment = analyzer.analyzeSentiment(review[1]);
+                sentimentDistribution.put(sentiment, sentimentDistribution.get(sentiment) + 1);
+                
+                System.out.printf("Review %2s: %-60s - %s\n", 
+                    review[0], 
+                    shortenText(review[1], 55), 
+                    sentiment);
+            }
+            
+            // Generate report
+            analyzer.generateReport(sentimentDistribution, reviews.size() - 1);
+            
+        } catch (IOException | CsvException e) {
+            e.printStackTrace();
+        }
+    }
+
+    public void initialize() {
+        // Set up pipeline properties
+        Properties props = new Properties();
+        props.setProperty("annotators", "tokenize, ssplit, parse, sentiment");
+        props.setProperty("coref.algorithm", "neural");
+        this.pipeline = new StanfordCoreNLP(props);
+    }
+
+    public List<String[]> loadReviews(String filePath) throws IOException, CsvException {
+        try (CSVReader reader = new CSVReader(new InputStreamReader(new ClassPathResource(filePath).getInputStream()))) {
+            return reader.readAll();
+        }
+    }
+
+    public String analyzeSentiment(String text) {
+        int mainSentiment = 0;
+        int longest = 0;
+        
+        Annotation annotation = pipeline.process(text);
+        for (CoreMap sentence : annotation.get(CoreAnnotations.SentencesAnnotation.class)) {
+            int sentiment = RNNCoreAnnotations.getPredictedClass(sentence.get(SentimentCoreAnnotations.SentimentAnnotatedTree.class));
+            String partText = sentence.toString();
+            
+            if (partText.length() > longest) {
+                mainSentiment = sentiment;
+                longest = partText.length();
+            }
+        }
+        
+        // Convert numeric sentiment to text
+        return switch (mainSentiment) {
+            case 0 -> "Very Negative";
+            case 1 -> "Negative";
+            case 2 -> "Neutral";
+            case 3 -> "Positive";
+            case 4 -> "Very Positive";
+            default -> "Neutral";
+        };
+    }
+
+    public void generateReport(Map<String, Integer> sentimentCounts, int totalReviews) {
+        System.out.println("\n=== Sentiment Analysis Report ===");
+        System.out.printf("Total Reviews Analyzed: %d\n\n", totalReviews);
+        
+        System.out.println("Sentiment Distribution:");
+        for (Map.Entry<String, Integer> entry : sentimentCounts.entrySet()) {
+            double percentage = (entry.getValue() * 100.0) / totalReviews;
+            System.out.printf("%-12s: %2d reviews (%5.1f%%) %s\n",
+                entry.getKey(),
+                entry.getValue(),
+                percentage,
+                generateBar(percentage));
+        }
+    }
+    
+    private static String generateBar(double percentage) {
+        int bars = (int) (percentage / 5);
+        return "[" + new String(new char[bars]).replace("\0", "=") + "]";
+    }
+    
+    private static String shortenText(String text, int maxLength) {
+        return text.length() > maxLength ? text.substring(0, maxLength - 3) + "..." : text;
+    }
+}

SentimentAnalysisLab/src/main/resources/application.properties

@@ -0,0 +1 @@
+spring.application.name=senti

SentimentAnalysisLab/src/main/resources/product_reviews.csv

@@ -0,0 +1,11 @@
+review_id,review_text
+1,"This product is absolutely amazing! Works perfectly."
+2,"Terrible quality. Broke after 2 days of use."
+3,"Good value for the price. Very satisfied."
+4,"Not what I expected. Poor packaging."
+5,"Excellent customer service and fast shipping."
+6,"The item arrived damaged. Very disappointed."
+7,"Works well but instructions could be better."
+8,"Best purchase I've made this year!"
+9,"Overpriced for what you get."
+10,"Highly recommend this to all my friends."

settings.gradle

@@ -7,4 +7,5 @@ include 'RetailManagementSystem:front-end'
 include 'InventoryManagementSystem'
 include 'SmartClinicManagementSystem:app'
 include 'SoftwareDevChatbot'
-include 'RegressionPredictionLab'
+include 'RegressionPredictionLab'
+include 'SentimentAnalysisLab'