> For the complete documentation index, see [llms.txt](https://docs.tgmetrics.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tgmetrics.ai/x-analytics/sentiment-score.md).

# Sentiment Score

The **Sentiment Score** section provides a day-by-day measurement of the overall tone of recent tweets related to the community. It reflects how positively or negatively the project is being discussed on **X**, helping to identify mood shifts, potential volatility, and market sentiment in real time.

This is one of the two dedicated components for **sentiment monitoring** within the **X Analytics** view.

<figure><img src="/files/lwCWMvEFpGFPv0HTtzL6" alt=""><figcaption><p>Sentiment score by day</p></figcaption></figure>

#### How It Works

* **Displays the average sentiment value** for each day, based on recent tweets, and replies mentioning the proejct.
* The score ranges from **0 to 100**, where higher values represent more positive sentiment.
* The line chart allows users to **track sentiment fluctuations** and detect trend reversals or sudden spikes in positivity or negativity.
* You can **toggle between 3D and 7D views** to compare short-term and weekly sentiment patterns.
* A **total sentiment value** is shown below the chart, representing the cumulative trend direction for the selected period.

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<figure><img src="/files/fPjz1aXtkfNPkMKp6Fn1" alt=""><figcaption><p>Overall Sentiment Score</p></figcaption></figure>

In thi section you have more details about the current sentiment about the community, with details about what is the % of positive, neutral and negative

#### How It Works

* **Displays a score from 0 to 100**, where higher values represent more positive sentiment.
* A **qualitative label** (e.g. *Slightly Bullish*, *Neutral*, *Bearish*) helps interpret the numerical score at a glance.
* Below the score, you’ll find a **polarity distribution** showing the percentage of:
  * **Positive** tweets (green)
  * **Neutral** tweets (blue)
  * **Negative** tweets (red)
* Sentiment is calculated using natural language processing (NLP) models that analyze tweet content and classify tone.