LeetCode #2284 — MEDIUM

Sender With Largest Word Count

Move from brute-force thinking to an efficient approach using array strategy.

Solve on LeetCode
The Problem

Problem Statement

You have a chat log of n messages. You are given two string arrays messages and senders where messages[i] is a message sent by senders[i].

A message is list of words that are separated by a single space with no leading or trailing spaces. The word count of a sender is the total number of words sent by the sender. Note that a sender may send more than one message.

Return the sender with the largest word count. If there is more than one sender with the largest word count, return the one with the lexicographically largest name.

Note:

  • Uppercase letters come before lowercase letters in lexicographical order.
  • "Alice" and "alice" are distinct.

Example 1:

Input: messages = ["Hello userTwooo","Hi userThree","Wonderful day Alice","Nice day userThree"], senders = ["Alice","userTwo","userThree","Alice"]
Output: "Alice"
Explanation: Alice sends a total of 2 + 3 = 5 words.
userTwo sends a total of 2 words.
userThree sends a total of 3 words.
Since Alice has the largest word count, we return "Alice".

Example 2:

Input: messages = ["How is leetcode for everyone","Leetcode is useful for practice"], senders = ["Bob","Charlie"]
Output: "Charlie"
Explanation: Bob sends a total of 5 words.
Charlie sends a total of 5 words.
Since there is a tie for the largest word count, we return the sender with the lexicographically larger name, Charlie.

Constraints:

  • n == messages.length == senders.length
  • 1 <= n <= 104
  • 1 <= messages[i].length <= 100
  • 1 <= senders[i].length <= 10
  • messages[i] consists of uppercase and lowercase English letters and ' '.
  • All the words in messages[i] are separated by a single space.
  • messages[i] does not have leading or trailing spaces.
  • senders[i] consists of uppercase and lowercase English letters only.

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: You have a chat log of n messages. You are given two string arrays messages and senders where messages[i] is a message sent by senders[i]. A message is list of words that are separated by a single space with no leading or trailing spaces. The word count of a sender is the total number of words sent by the sender. Note that a sender may send more than one message. Return the sender with the largest word count. If there is more than one sender with the largest word count, return the one with the lexicographically largest name. Note: Uppercase letters come before lowercase letters in lexicographical order. "Alice" and "alice" are distinct.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Hash Map

Example 1

["Hello userTwooo","Hi userThree","Wonderful day Alice","Nice day userThree"]
["Alice","userTwo","userThree","Alice"]

Example 2

["How is leetcode for everyone","Leetcode is useful for practice"]
["Bob","Charlie"]

Related Problems

  • Top K Frequent Elements (top-k-frequent-elements)
  • Top K Frequent Words (top-k-frequent-words)
Step 02

Core Insight

What unlocks the optimal approach

  • The number of words in a message is equal to the number of spaces + 1.
  • Use a hash map to count the total number of words from each sender.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Upper-end input sizes
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #2284: Sender With Largest Word Count
class Solution {
    public String largestWordCount(String[] messages, String[] senders) {
        Map<String, Integer> cnt = new HashMap<>(senders.length);
        for (int i = 0; i < messages.length; ++i) {
            int v = 1;
            for (int j = 0; j < messages[i].length(); ++j) {
                if (messages[i].charAt(j) == ' ') {
                    ++v;
                }
            }
            cnt.merge(senders[i], v, Integer::sum);
        }
        String ans = senders[0];
        for (var e : cnt.entrySet()) {
            String k = e.getKey();
            int v = e.getValue();
            if (cnt.get(ans) < v || (cnt.get(ans) == v && ans.compareTo(k) < 0)) {
                ans = k;
            }
        }
        return ans;
    }
}
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n + L)
Space
O(n)

Approach Breakdown

BRUTE FORCE
O(n²) time
O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED
O(n) time
O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.