Google Play - Reviews Sentiment Analysis Analyze thousands of Google Play Store reviews in seconds using cutting-edge Artificial Intelligence (Groq/LLMs). This Actor doesn't just fetch data; it acts as an automated market researcher by identifying strengths (PROS) and weaknesses (CONS) of your app or your competitors. Find out what your customers love or hate! --- ## Key Features Unlike standard scrapers, this tool provides actionable insights through a sophisticated AI pipeline: * Intelligent Extraction: Leverages a high-performance Google Play scraper to retrieve the most recent user feedback. * AI-Powered Map-Reduce: Processes reviews in chunks using ultra-fast LLMs for initial analysis and a high-reasoning model for the final synthesis. * Theme Recognition: Automatically detects recurring themes (e.g., "login bugs", "intuitive UI") and quantifies their frequency. * Executive Summary: Generates a final textual report that directly addresses your strategic questions. --- ## How It Works The Actor follows a professional three-stage workflow: 1. Scraping: Triggers the google-play-scraper to collect raw review data. 2. AI Analysis (Groq): Sends processed text to Groq for near-instant categorization of feedback into PROS and CONS. 3. Synthesis: Aggregates the data into a "Top 5" list of strengths and weaknesses, followed by a detailed executive summary. --- ## Input The Actor accepts the following parameters: | Field | Type | Default | Description | | :--- | :--- | :--- | :--- | | appId | String | - | The unique App ID (e.g., com.instagram.android). Required. | | maxReviewsToProcess | Number | 1000 | Maximum number of reviews to analyze. | | summaryPrompt | String | "Generates a summary..." | Specific instructions for the AI on what to focus on. | --- ## Output Example Results are saved to the dataset in a clean JSON format: json { "app_id": "com.example.app", "summary_report": "The app is highly praised for its speed, but several users have reported issues with the latest payment system update...", "top_pros": [ [150, "Loading Speed"], [85, "Clean UI"] ], "top_cons": [ [40, "Checkout Crash"], [12, "Lack of Dark Mode"] ], "total_reviews_processed": 1000 } ## Pricing (Pay-per-Event) This Actor uses a value-based pricing model. Instead of paying for computer resources (RAM/CPU), you only pay when you get a result. * Trigger Event: AnalisysReportCompleted * Cost: Charged only when the AI successfully generates and saves the final report to the dataset. * Safety Net: You will not be charged if the scraper finds no reviews, if the AI API is down, or if the run is aborted before the final analysis is completed. --- ## Use Cases | Case | Description | | :--- | :--- | | Competitor Audit | Identify why users are leaving your competitors' apps and capitalize on their weaknesses. | | Product Strategy | Prioritize your roadmap by focusing on the most requested features or most frequent bugs. | | Ad Copywriting | Use real user language and praised features directly in your marketing materials for better conversion. | | Sentiment Monitoring | Track how user perception changes after a major update or redesign. | ---