Enterprise AI Analysis
Low-Code GPT-4 Workflow for Indonesian Sentiment Analysis with Dify
This paper presents a groundbreaking low-code, end-to-end sentiment analysis workflow built on Dify and GPT-4, specifically tailored for Indonesian text. It demonstrates how businesses can achieve rich, structured sentiment insights with minimal engineering, enabling agile, data-driven decisions on complex, informal language data.
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Texts Processed/Hour
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Cost per 1,000 Texts
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Rapid Deployment
Deep Analysis & Enterprise Applications
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Streamlined Low-Code AI Workflow
Our research demonstrates a robust, low-code sentiment analysis pipeline that integrates data collection, LLM inference, and visualization. Utilizing Dify as an orchestration platform, we enable rapid deployment of sophisticated AI capabilities without extensive coding or specialized ML teams. This modular approach ensures flexibility and reusability across diverse datasets.
Enterprise Process Flow
Data Collection (Octoparse)
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Low-Code LLM Workflow (Dify)
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Structured JSON Output
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Interactive Dashboard (Python)
LLM vs. Traditional Models: A Performance Review
We benchmarked our GPT-4 workflow against IndoBERT, a traditional supervised model, on Indonesian protest tweets. While showing different labeling behaviors, GPT-4 offers richer, more nuanced outputs. The cost-efficiency of GPT-40-mini makes it highly practical for large-scale operations.
| Feature | LLM (GPT-4) Approach | Traditional (IndoBERT) Approach |
|---|---|---|
| Model Type | Large Language Model (GPT-4 turbo/mini) | Supervised BERT-based Transformer |
| Development Effort | Low-code, few-shot prompting, no fine-tuning required. | High: Requires labeled data, preprocessing, model training, and management. |
| Output Richness | Rich: Sentiment score, label, categories, keywords. | Single label (e.g., positive, neutral, negative). |
| Scalability & Cost | High throughput (1800+ texts/h), variable cost ($0.09 - $5.80 / 1K texts). | High throughput, generally fixed deployment cost. |
| Language Nuance | Context-sensitive, captures finer-grained polarity. | Dataset-dependent, may struggle with sarcasm/complex context. |
$0.09
Per 1,000 Texts (GPT-40-mini) - Enabling cost-effective, high-volume sentiment analysis for Indonesian text.
Case Study: Indonesian Political Sentiment
We applied our low-code workflow to 353 Indonesian protest-related tweets concerning a high-profile political incident. This dataset posed typical challenges of informal language and context-dependent sentiment. Using Octoparse for data collection, Dify for GPT-4 inference, and a custom Python dashboard for visualization, we successfully extracted nuanced sentiment insights.
The system provided detailed outputs including sentiment scores, categories, and keywords, which were then interactively visualized to help understand public opinion trends over time. This demonstrates the framework's capability to deliver actionable intelligence from real-world, time-sensitive Indonesian social media data.
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Annual Savings Potential
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Your Path to Enterprise AI Implementation
Our proven methodology ensures a smooth and effective integration of AI into your existing operations, delivering value at every stage.
Phase 1: Initial Setup & Data Ingestion
Integrate data sources (e.g., Octoparse) with Dify; upload initial datasets for processing, establishing the foundational data pipeline.
Phase 2: Prompt Engineering & Workflow Configuration
Design and configure JSON-based prompts, define routing logic (e.g., Multisentiment flag) within Dify to tailor AI behavior to your needs.
Phase 3: LLM Inference & Output Generation
Execute batch processing using GPT-4 variants via Dify, generating structured sentiment outputs for detailed analysis.
Phase 4: Dashboard Integration & Validation
Connect AI outputs to the interactive dashboard, visualize results, and conduct initial validation against baselines for accuracy.
Phase 5: Iterative Refinement & Expansion
Continuously optimize the workflow for cost/latency, extend to new domains, and integrate human feedback for ongoing improvement.
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