The mood from around the world, right now.

Sentiment results calculated from real-time Twitter data, and illustrated live on a series of isomorphic D3.js data visualisations.
The sentiment analysis algorithm is based on the lexicon approach, and written in CoffeeScript, all code is open sauce on GitHub


0 positive, and 0 negative tweets analysed since landing on this page

+/- Proportions

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Live Sentiment Tweets by Location

Dots show (a set of) real-time tweets, where color indicates sentiment (green = positive, red = negative, and all shades in between). Hover over a point for more information.

More geo-views:Heat Map3D GlobeRegional Map

Bullet Chart

The bullet chart shows volume of tweets analysed since you landed on this page (x-axis), and what proportion of these are positive (green) and negative (red).

Recent Positive Tweets

How many tweets to show

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Recent Negative Tweets

How many tweets to show

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About Sentiment Sweep

Sentiment Sweep aims to captcha the mood of the internet, either overall or towards a specific topic. It does this by analysing real-time Twitter data, and calculating how positive or negative each Tweet it.

A series of dynamic data visualisations are then used to illustrate the results, and find trends between sentiment and other factors such as time of day, location, topic, country, people etc


Source & Documentation

Sentiment Sweep is open sauce and distributed under the MIT License. All code is published on GitHub (lissy93/twitter-sentiment-visualisations).

The project is primarily written in Node.js CoffeeScript, and D3.js is used for the majority of the data visualisations. is used to manage the live interactions, and the data cache is MongoDB. Everything is hosted on a CentOS VPS.

All code is thoroughly documented and tested.

More Sentiment Data Visualisations

Click one of the links below to generate the chart with the latest Twitter data