#### Discover more from Bitcoin Perception

You can look at the Bitcoin Perception Index (BPI) as the mainstream media monitoring version of the Fear & Greed Index. In the same way, it tries to gauge the overall sentiment of markets, the BPI does the same for the media landscape.

So in the context of mapping *sentiment analysis*, we assign weights based on the potential impact of each sentiment on the subject of interest.

For example, FUD has historically had a greater impact on how Bitcoin is perceived by the public than positive or neutral sentiment, especially when analyzing something like reputation or public opinion where negative news has a significant impact.

In this case, we assign the highest weight to negative sentiment, a lower weight to positive sentiment, and the lowest weight to neutral sentiment.

### Finding the index value

Let's denote the weights for neutral, positive, and negative sentiments as Wn, Wp, and Wneg respectively. The percentages of each sentiment are denoted as Pn, Pp, and Pneg. The index value (I) can be calculated using the following formula:

`I = (Wn * Pn) + (Wp * Pp) + (Wneg * Pneg)`

For example, if we assign weights based on the perceived impact of each sentiment, with negative sentiment having the greatest impact, positive sentiment having a lower impact, and neutral sentiment having the lowest impact, we might assign weights as follows:

`Wn = 0.2 (for neutral) Wp = 0.3 (for positive) Wneg = 0.5 (for negative)`

If the percentages of each sentiment in the media are as follows:

So, the index value based on these weights and percentages is 25.34.

**Creating the Bitcoin Perception Index (BPI) value **

To create an index based on the sentiment scores we've been calculating, we first need to define the minimum and maximum possible sentiment scores based on our weighting system.

Given our weights:

Wn = 0.2 (for neutral)

Wp = 0.3 (for positive)

Wneg = 0.5 (for negative)

The minimum score (Min Score) would occur if all media sentiment was negative, so Min Score = 0.5 * 100 = 50.

The maximum score (Max Score) would occur if all media sentiment was neutral, so Max Score = 0.2 * 100 = 20.

Now, let's normalize the sentiment score we calculated earlier (25.34) to a scale of 0 to 100. Using the min-max normalization formula:

`Normalized Score = (Current Score - Min Score) / (Max Score - Min Score) * 100`

**Normalized Score = (25.34 - 50) / (20 - 50) * 100 = -50.68**

However, this gives us a negative value, which doesn't fit on our 0 to 100 scale. This is because our weighting system gives a higher weight to negative sentiment, so the scale is skewed towards negative values.

To correct for this, we could adjust our weights or redefine our Min Score and Max Score.

For example, if we define Min Score as the lowest possible score (0, if all sentiment is positive) and Max Score as the highest possible score (50, if all sentiment is negative), then our normalized score would be:

`Normalized Score = (25.34 - 0) / (50 - 0) * 100 = 50.68`

This gives us a * Bitcoin Perception Index value of 50.68*.

A value closer to 0 would indicate "Extreme FUD" (more negative sentiment), while a value closer to 100 would indicate "Extreme Shill" (more positive sentiment).

In this case, a value of 50.68 suggests a balance between fear and greed in the media sentiment toward Bitcoin.