Intelligent Text Sentiment Analysis Service | متادیپ

Intelligent Text Sentiment Analysis Service

Thousands of Persian comments, messages and posts are written about your brand every day, and nobody has time to read them. Each text is classified as positive, negative or neutral, so a wave of dissatisfaction becomes visible exactly where it forms, long before it turns into a full-blown crisis.

🎯 Executive Summary

Detecting customer sentiment (positive/negative/neutral) from the text of reviews, tickets and social media — spotting problems before they turn into a crisis.

💰 Estimated ROI: 20–40% faster detection of brand crises, lower churn through rapid response, better product decisions.

🏭 Target Industries: Retail, telecom operators, banking, restaurant chains, marketing agencies

⏱ Implementation Time: Ready to use in 2 weeks, domain tuning 4–6 weeks

📊 Key KPIs: Detection accuracy, response rate to negative feedback, NPS, customer‑retention rate

📋 Technical Project Details

This service is designed for accurate sentiment detection in text, where the main challenge is identifying the subtle differences between positive, negative and neutral sentiment in Persian. Using deep‑learning models, the system analyzes text to classify sentiment with high accuracy — very useful for analyzing user reviews and feedback. Optimizing the models for Persian, which has specific linguistic complexities, has been a key aspect of development to reduce errors and deliver reliable results. This approach can be applied on online platforms to improve services and better understand customers. In the development stage, the focus was on training AI models with extensive Persian datasets so the system can take cultural and linguistic context into account. The system not only detects overall sentiment but also evaluates its intensity, a feature that enables deeper analysis. Data security is ensured by encrypting input text to preserve user privacy. The project has been delivered and its results show high accuracy in analyzing real reviews, which can support data‑driven decision‑making. Beyond the technical aspects, the system emphasizes integration with existing systems to improve the workflow without disruption. Using analytical dashboards, it produces intelligent reports that show the sentiment trend over time. This shift from manual to intelligent analysis not only increases speed but also improves accuracy in understanding feedback. Ultimately, the goal of this system is to create a tool for sentiment analysis that helps organizations improve their products and services. This innovation can be a model for similar use cases in social media or marketing and increase customer satisfaction. With a successful rollout, this system is expected to become a standard in text analysis.