With the popularity of social media, text sentiment has become a significant factor, even in trading and investing. So much so, that there becomes many ways we can view this sentiment. First off, we can look at the text sentiment contextually. That means looking at the actual texts themselves and internalizing it to come to a decision.

Take a look at this video for an example of streaming texts from Twitter when searching for “EURUSD”:
As you can see this example uses a Python application to source texts of the major cross pair EURUSD. By the way, the source code for this application can be found here at Github.

Streaming news and texts becomes vital for a day trader, but what about streamlined texts? Is there a way we can quantify and score text sentiment without looking at every single text? The answer is “Yes”. There is a way to do this, many people turn to fintech companies; such as, Dataminr for news, or estimize for earnings estimates.

If you prefer a sentiment heatmap of all stocks on your watchlist, you can get this type of coverage at Social Martket Analytics. Here’s a heatmap that shows scored sentiment represented by different colors:

As you can see, text sentiment can be contextual as well as scored and streamlined. This helps traders make estimates and weighted decisions. Furthermore, it can help in building better trading systems and technical indicators.
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