**Papo (https://papo.global/) uses artificial intelligence to analyze conversation messages in real-time and classify customer sentiment as positive, negative, or neutral. This analysis allows support and sales teams to proactively identify dissatisfied customers, business opportunities, and prioritize the most critical interactions through tools like Synapse's Customer Radar and automation rules.**

Understanding the emotion behind a customer's message is crucial for providing quality service. A request for "help" might have a neutral tone, while "I need help NOW, nothing works" carries a clearly negative sentiment. Papo's AI has been trained to capture these nuances, transforming the conversation's tone into actionable data for your operation.

| Sentiment | What it means | Strategic Use Example |
| :--- | :--- | :--- |
| **Positive** | Satisfied customer, praising the product, agreeing to a solution, or showing enthusiasm. | Identify brand advocates, request a testimonial, or direct to an upgrade (upsell) offer. |
| **Negative** | Frustrated customer, complaining about a problem, mentioning competitors, or expressing disappointment. | Prioritize service, apply an "Urgent" tag, notify a supervisor, or trigger a workflow to prevent cancellation (churn). |
| **Neutral** | Customer asking an objective question, requesting information, confirming data, or in a purely transactional interaction. | Follow the standard service flow, collect information, or direct to a quick response or Help Center article. |

## How Papo's AI Analyzes Sentiment

Papo's artificial intelligence is not based solely on isolated keywords. The system has been trained with millions of conversation examples to understand the context, intent, and linguistic patterns that indicate the customer's emotional state.

The analysis considers multiple factors:
*   **Word Choice:** The presence of strong adjectives like "incredible," "perfect," and "excellent" signals positivity, while terms like "problem," "disappointed," "error," and "doesn't work" indicate negative sentiment.
*   **Sentence Context:** The AI evaluates the sentence as a whole. "I didn't have any problems" is a positive phrase, despite containing the word "problem."
*   **Intensity:** The use of capital letters, word repetition ("never, never again"), and excessive punctuation (!!!, ???) are interpreted as intensifiers of the expressed sentiment.

This analysis directly feeds into intelligence modules like **Synapse**. In it, the **Customer Radar** uses sentiment as one of the main factors to calculate a contact's "criticality." A conversation with persistent negative sentiment will have higher criticality, signaling to the team that the customer needs immediate attention to prevent churn.

## How to Use Sentiment Analysis in Automations

The true power of sentiment analysis lies in using it to automate actions and optimize processes. At Papo, this is primarily done through **Automation Rules**. You can configure rules that are triggered based on the sentiment detected in a message.

**Practical examples of sentiment-based automation:**

1.  **Prioritizing Dissatisfied Customers:**
    *   **Trigger:** When a message is received.
    *   **Condition:** If the message sentiment is `Negative`.
    *   **Action:** Add the tag "At-Risk Customer" and assign the conversation to a senior agent or a retention team.

2.  **Identifying Feedback Opportunities:**
    *   **Trigger:** When a conversation is resolved.
    *   **Condition:** If the customer's last message had `Positive` sentiment.
    *   **Action:** Schedule an automatic message to be sent after 24 hours asking the customer to leave a review or testimonial.

3.  **Triage and Organization:**
    *   **Trigger:** When a new conversation is created.
    *   **Condition:** If the first message's sentiment is `Negative`.
    *   **Action:** Mark the conversation as `Pending` and with high priority so it's the first in line to be handled.

By automating the application of tags based on sentiment, you also enrich your reports. The **Tags Report** will then show not only the most common topics but also the proportion of satisfied and dissatisfied customers, providing a clear view of your service's health.

## Step-by-step: Creating a Sentiment-Based Automation

Below is an example of how to create a rule to automatically escalate service for a dissatisfied customer.

| Step | Action | Configuration Details |
| :--- | :--- | :--- |
| **1. Access Automations** | In the Papo dashboard, navigate to the **Automations** module and select the option to create a new **Rule**. | Give the rule a clear name, such as "Escalate Negative Service". |
| **2. Define the Trigger** | Choose the event that will initiate the rule check. | Select the **"Message Created"** trigger and configure it to be activated only by customer messages. |
| **3. Add the Condition** | Specify the main condition that must be met for the rule to execute. | Add the condition: `Message Sentiment` > `is` > `Negative`. |
| **4. (Optional) Refine the Rule** | Add other conditions to make the rule more specific. | You can add a condition like `Inbox` > `is` > `Technical Support` so that the rule only applies to that channel. |
| **5. Define the Actions** | Determine what the platform should do when the conditions are true. | Add the action **"Add Tag"** and select the tag "Urgent - Dissatisfied". Then, add the action **"Assign to Team"** and choose the "Level 2 Support" team. |

## Frequently Asked Questions

### Is AI sentiment analysis 100% accurate?
Sentiment analysis is based on machine learning models and has high accuracy, but it is not infallible. Sarcasm, irony, and complex cultural contexts can occasionally be misinterpreted. It should be used as a powerful aid and triage tool, but human judgment remains valuable for sensitive cases.

### In which languages does sentiment analysis work?
The Papo platform is developed to support multiple languages. For a complete and updated list of languages covered by sentiment analysis, please consult the official documentation or the features page on the Papo website.

### What is the difference between sentiment analysis and AI tagging?
These are two complementary functionalities. **Sentiment analysis** identifies the emotion or tone of the message (positive, negative, neutral). **AI tagging** identifies the subject or topic of the conversation (e.g., "Billing Inquiry," "Access Problem"). Together, they offer complete context: a conversation with `Negative` sentiment and an `Access Problem` tag is a clear priority.

### How does sentiment analysis help prevent churn (customer loss)?
By detecting frustration and dissatisfaction in real-time, sentiment analysis enables proactive action. Instead of waiting for the customer to formalize a complaint or simply stop using your service, automations can escalate the service to a specialist or notify a manager immediately, increasing the chances of reversing the situation and retaining the customer.

### Can I create reports based on customer sentiment?
Yes. By using automation rules to apply tags like "Satisfied" or "Dissatisfied" based on detected sentiment, you can use the Tags Report to visualize the distribution and trends of customer satisfaction over time, by channel, or by team.

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