# Analyse sentiment in emails -  Sentiment Analysis

## Overview

Our sentiment analysis pattern, available in [Marketplace](/enate-help/builder/builder-2021.1/integrations-marketplace.md) in Builder enables the analysis of the content of incoming emails and determine if, for example, they are positive or negative. This assessment can be passed back into Enate Work Manager so that Agents can tell at a glance what the tone of the mail is as headline information as they start to deal with it. 

This pattern is available via the following providers:

* [EnateAI](#enateai-setup)
* [UiPath Communications Mining](#uipath-communications-mining-setup)

## EnateAI Setup

EnateAI is a zero configuration option supplied by Enate which is a simple click to activate.

<figure><img src="/files/8U2iQCx1gujtDwmyl4SV" alt=""><figcaption></figcaption></figure>

## UiPath Communications Mining Setup

UiPath Communications Mining provides communication analysis and to help automate your processes.&#x20;

Note that the Sentiment Analysis pattern works slightly differently when using UiPath Communications Mining as your provider. Communications in incoming emails are still analysed, but the assessment is not passed back into Enate Work Manager. Instead, the assessment remains in you UiPath Communications Mining system, allowing you to auto-tag and categorise emails in a certain way according to how you've set UiPath Communications Mining up.

The following video takes you through how to setup UiPath Communications Mining for sentiment analysis.

{% embed url="<https://enate.cdn.spotlightr.com/watch/MTM4ODM1OA==>" %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.enate.net/enate-help/integrations/enate-integrations/analyse-sentiment-in-emails-sentiment-analysis.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.