# Sentiment

**Overview**\
The Sentiment metric measures the tone of discussion around a keyword, brand, or topic across social and web data. It classifies posts as **positive, negative, or neutral**, and aggregates them into a distribution and a single **net sentiment score**. This helps quantify *how people feel*, not just how much they are talking.

**Historical Length**\
Varies by platform and data availability, but typically spans **multiple years of historical data** where supported. Allows users to track how perception evolves over time, especially around product launches, updates, or events.

**Granularity**\
Available at **daily aggregation**, with the ability to view sentiment snapshots tied to specific time windows or spikes in discussion.

**Update Frequency**\
Sentiment data is **updated daily**, reflecting the most recent shifts in tone as new posts and discussions are ingested.

**Methodology**\
Sentiment is derived using natural language processing models that analyze the tone and context of posts. Each post is classified into one of three categories:

* **Positive**: Favorable or supportive language
* **Negative**: Criticism, complaints, or dissatisfaction
* **Neutral**: Informational or non-opinionated mentions

These are then aggregated into:

* **Percentage breakdown** (Positive / Negative / Neutral)
* **Net Sentiment Score** (range: -1 to +1)

The score is calculated by weighting positive vs. negative mentions, providing a directional view of overall sentiment.

**Use Cases**

* Identify **user frustration or product pain points** early
* Track **consumer perception shifts** after launches or updates
* Validate whether **rising discussion is positive or negative**
* Compare **brand perception vs. competitors**
* Support **earnings or demand narratives** with qualitative context

**Example Visualization**

<figure><img src="/files/sS3fV9ZWc9T1jev8dO3s" alt=""><figcaption></figcaption></figure>

In the example above:

* **P: 24% | N: 58% | U: 18% | Score: -0.33**

This indicates:

* Majority of conversation is **negative**
* Overall tone is **skewing unfavorable**
* Likely driven by complaints or friction around the topic


---

# 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.tickertrends.io/tickertrends/documentation-v2/2.-data-coverage-and-data-types/2.2-data-types/sentiment.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.