Calculate Repeated Words of Two Variables in Java Using Scanner

Use this interactive calculator to compare two text variables, identify repeated words shared between them, and visualize the overlap. Below the tool, you will find an expert guide on implementing the same logic in Java with Scanner, tokenization, normalization, loops, collections, and practical code structure.

Repeated Words Calculator

Variable 1 text

Variable 2 text

Case sensitivity

Match type

Punctuation handling

Minimum word length

Enter two text values and click Calculate to see repeated words, frequencies, and summary metrics.

Comparison Chart

How the tool works:

Reads two text inputs like two Java variables.
Splits text into words.
Normalizes case and punctuation based on your options.
Finds common words and displays counts visually.

Expert Guide: How to Calculate Repeated Words of Two Variables in Java Using Scanner

If you want to calculate repeated words of two variables in Java using Scanner, the core idea is simple: read two pieces of input, split them into tokens, normalize the tokens, compare the words, and then count the overlap. In practice, however, the quality of your result depends on several technical decisions. You need to decide whether matching should be case sensitive, whether punctuation should be removed, whether duplicate words inside the same variable should count once or multiple times, and which data structure provides the most efficient comparison.

In Java, Scanner is commonly used to read user input from the console. You can use it to capture two lines of text that represent two variables. Once these values are stored as strings, your program can process them with methods like split(), toLowerCase(), and regular expressions. For more advanced counting, classes such as HashMap and HashSet are especially useful.

What “repeated words of two variables” usually means

Most developers use this phrase to mean common words that appear in both string variables. For example:

String a = “java scanner reads words”; String b = “scanner java compares words”;

The repeated words between these two variables are java, scanner, and words. If you are counting unique overlap, the answer is 3. If you are counting frequency based overlap, the answer can change depending on how many times each word appears in each variable.

Basic Java workflow using Scanner

Create a Scanner object.
Read the first variable as a full line.
Read the second variable as a full line.
Convert text to a standard format, often lowercase.
Remove punctuation if needed.
Split each line into words.
Store words in a set or map.
Compare the collections and count common entries.

Here is a simple structure:

import java.util.*; public class RepeatedWordsExample { public static void main(String[] args) { Scanner sc = new Scanner(System.in); System.out.print(“Enter first sentence: “); String first = sc.nextLine(); System.out.print(“Enter second sentence: “); String second = sc.nextLine(); first = first.toLowerCase().replaceAll(“[^a-z0-9\\s]”, “”); second = second.toLowerCase().replaceAll(“[^a-z0-9\\s]”, “”); String[] words1 = first.split(“\\s+”); String[] words2 = second.split(“\\s+”); Set set1 = new HashSet<>(Arrays.asList(words1)); Set set2 = new HashSet<>(Arrays.asList(words2)); set1.retainAll(set2); System.out.println(“Repeated words: ” + set1); System.out.println(“Count: ” + set1.size()); sc.close(); } }

This version counts unique repeated words. It is ideal when you want to know which words are shared without counting duplicates more than once. It is also efficient because HashSet offers fast lookups in average use cases.

Unique overlap versus frequency based overlap

One of the most important design choices is deciding how to count repeated words. There are two common models:

Unique overlap: each repeated word counts once, even if it appears many times in either variable.
Frequency based overlap: each repeated word counts by the minimum number of appearances across both variables.

Suppose the first variable contains “java java scanner words” and the second contains “java scanner scanner words”. The unique overlap is 3: java, scanner, words. The frequency based overlap is also 3 here if computed as the minimum matches per word: java = 1, scanner = 1, words = 1. But if the second variable were “java java scanner words”, the frequency based count would become 4 because java contributes 2.

Comparison table: counting model examples

Variable 1	Variable 2	Unique repeated words	Frequency based overlap
java scanner words	scanner java compares words	3	3
java java scanner	java scanner scanner	2	2
code code code test	code test test	2	2
apple banana apple banana	banana apple apple grape	2	3

Why normalization matters

Normalization is the step that makes your comparison more accurate. Without normalization, Java treats “Java” and “java” as different strings. It also treats “scanner,” and “scanner” as different values if punctuation is left in place. For most text comparison tasks, you should normalize to lowercase and strip punctuation before splitting.

A common line of code looks like this:

text = text.toLowerCase().replaceAll(“[^a-z0-9\\s]”, “”);

This keeps letters, digits, and spaces while removing punctuation. If your input can include non English characters, you may need a more advanced Unicode aware regular expression. Still, for beginner and interview level Java programs, the pattern above is widely used and easy to explain.

Using HashMap for precise word counts

If your requirement is to count repeated words based on frequency, HashMap<String, Integer> is a stronger solution than a set. A map lets you record how many times each word appears in each variable, then compare the counts.

Map map1 = new HashMap<>(); for (String word : words1) { map1.put(word, map1.getOrDefault(word, 0) + 1); } Map map2 = new HashMap<>(); for (String word : words2) { map2.put(word, map2.getOrDefault(word, 0) + 1); } int repeatedCount = 0; for (String word : map1.keySet()) { if (map2.containsKey(word)) { repeatedCount += Math.min(map1.get(word), map2.get(word)); } }

This pattern is reliable because it handles duplicates correctly. It is the preferred approach when your assignment, coding challenge, or production logic asks for exact repeated word frequency rather than just common membership.

Practical performance statistics

Text comparison is usually lightweight for console programs, but data structures still matter. Java collection operations are efficient enough for typical user input, and choosing the right structure improves clarity and speed. The following table summarizes commonly accepted average performance characteristics for standard Java collections.

Operation	HashSet average	HashMap average	ArrayList average
Contains lookup	O(1)	O(1)	O(n)
Insert item	O(1)	O(1)	O(1) amortized
Count duplicates	Not ideal	Excellent	Requires manual scan
Best use here	Unique overlap	Frequency overlap	Basic learning examples

Common mistakes students make

Using next() instead of nextLine() when full sentences are required.
Forgetting to normalize case, which causes missed matches.
Leaving punctuation attached to words.
Counting duplicates incorrectly by using only a set when frequency is needed.
Not handling extra spaces, leading to empty tokens.
Closing Scanner too early or mixing numeric input with line input incorrectly.

Recommended coding pattern with Scanner

If you are writing a clean beginner friendly program, use this pattern:

Prompt the user clearly.
Read both strings with nextLine().
Normalize each string.
Split using \\s+ so multiple spaces are handled properly.
Use HashSet for unique repeated words or HashMap for frequencies.
Print both the repeated words and the count.

When Scanner is the right choice

Scanner is ideal for classroom exercises, command line tools, and simple input driven Java programs. It is easy to understand and integrates well with string processing. If you are dealing with very large files, buffered input approaches may be faster, but for comparing two variables typed by a user, Scanner is fully appropriate and widely taught.

Authoritative references for deeper study

For stronger fundamentals on Java input, strings, and data structures, review these educational and public resources:

Sample full approach in plain English

Imagine a user enters two sentences. Your Java program reads both sentences through Scanner. You convert both to lowercase, remove punctuation, split them into words, and store the words. If the assignment asks “find repeated words,” a set based intersection is usually enough. If the assignment asks “count how many repeated times words occur,” a map based frequency comparison is better. Both are valid. The difference lies in the exact business rule behind the word “repeated.”

In interviews and coursework, always explain your assumption. Say something like: “I will count repeated words as unique common words unless frequency based counting is required.” That short clarification shows good engineering thinking and prevents ambiguity.

Final best practices

Use nextLine() for full sentence input.
Normalize before comparing.
Use HashSet for unique overlaps.
Use HashMap for repeated frequency counts.
Handle empty input safely.
Print clear, readable output for the user.

By combining Scanner with string normalization and the right collection type, you can accurately calculate repeated words of two variables in Java. The calculator above demonstrates the same logic interactively: it reads two text values, applies your chosen rules, computes the repeated words, and visualizes the result. If you implement the same steps in Java, your program will be correct, readable, and efficient for common text comparison tasks.

Calculate Repeated Words Of Two Variables In Java Using Scanner