Keyword Counter

😫 The Pain Point

You have 1,000 feedback comments. You want to know what customers are talking about most? “Good”, “Expensive”, “Broken”, or “Slow”? Reading 1,000 comments manually is impossible.

🚀 Agentic Solution

Text Miner: Breaks down sentences into words, counts them, and visualizes the data.

Key Features:

• Stopwords: Automatically removes filler words like “the”, “is”, “and”.
• Word Cloud: Generates a visual cloud where bigger words = more frequent.

⚔️ Phase 1: Commander (Quick Fix)

For a quick pulse check.

Prompt:

“I have reviews.txt. Count the frequency of each word, ignoring common English stopwords. Print the top 10 most used words.”

Result: Top 10 buzzwords.

🏗️ Phase 2: Architect (Permanent Tool)

For Content/SEO Strategists.

Engineering Prompt:

**Role:** Python NLP Processor
**Task:** Create a "Keyword Frequency Analyzer".
**Requirements:**
1.  **GUI:**
    *   Input Text box OR Upload .txt file.
    *   "Analyze" button.
2.  **Logic:**
    *   Tokenize text.
    *   Remove English Stopwords (use `nltk` or a hardcoded list).
    *   Count frequency using `collections.Counter`.
    *   Display Top 20 keywords in a Table.
    *   *Bonus:* Generate a WordCloud image.
3.  **Deliverables:** `keyword_tool.py`, `run.bat` (Windows), `run.sh` (Mac).

🧠 Prompt Decoding

• Stopwords: Without removing “stopwords” (the, is, at), your top 10 list would be useless. The prompt ensures meaningful analysis by filtering the noise.

🛠️ Instructions

1. Copy Prompt -> Paste -> Run.
2. Paste Text -> Analyze.