Hi solarion, thanks for your comment. Sorry that you found our website down, we're still optimising our infrastructure. I just upgraded our database to a more powerfull one and we corrected some error. Hope next time you'll find our website up and running bro 

You're perfectly right and it's in our ToDo list  

Hello, merci pour ton retour.

Alors non notre projet n'est pas sur un repo publique. Par contre au niveau de l'analyse de ces données nous fonctionnons avec plusieurs instances sur Amazon. L'une pour le streaming des tweets qui les passe brut dans une base de données avec une valeur checked=0. Pour le deeplearning nous avons une API qui tourne sur plusieurs instances C5xLarge et qui analyse en volume tous les tweets unchecked de la base de donnée.

Bonjour à tous,

Hello fellow SwissBoo, thanks for your proposal. Yep we need a lot of beta testers  feedback help us keep motivation and improve the tool that people would use and find useful.

No we collect and analyse data since 24th february, it collects data each minutes 24/7. For historical data we already downloaded some data from different sources but it's not in use right now. It's part of the optimisation once we adapt our algorithm to work with technical data (volume, average, etc.)


Thanks for your feedback and the reference. Entity tagging needs some optimisation and it's not what we use for the sentiment at the moment, we use the overall sentiment of each tweets. However we integrated the entity features in the API for a test phase.
Our algorithm already use Stanford CoreNLP to determine entities and coreferences associated with each entity. It then determines the sentiment on these (at character level). The problem is that it is a bit simple for now - it only looks for capitalised nouns which are also subjects.

I'll soon update this so it also picks up lowercase sentence objects (not subjects) like “bitcoin” as an entity. Then the process remains the same - coreference -> new active sentence generation -> sentiment analysis.

Will post when this is updated.

Yes i’s categoized by positive and negative and we calculate the neutral base on the score.

Our datasource is everything that include bitcoin or other coin keyword. Could be a good idea to follow some relevant people. Actually we have a lot of noise in our data and it’s another improvment to clean them.

Are you also a crypto investor? As investor ourself we plan to use our own tool to make decision and get a good overview of each coin.

Thanks for your feedback.

I'll take your advice and we'll explain it on the website soon.

Actually it's a recurrent neural network using LSTM to analyse tweets sentiment. We trained the model with 1 million classified tweets about everything. We plan to get or make a training set based only on trading, our model will be more accurate.

The datapoints represents the number of tweets we analysed, it's categorized by positive, neutral and negative and it's represented on the graph.

The try link just wait for a sentence and return the entity sentiment (if you select entity-based analysis) or the overall sentiment. It's not perfect and we are currently working on algorithm improvement, we expect some feedback in order to know if people would use that kind of information/service. One of our next goal is to cross technical and sentiment analysis in order to be even more accurate about the price prediction per coin.

Hi guys, posting here to get some feedback about a new project me a friend just released in beta version.
It's a cryptocurrency sentiment analysing tool that use deep learning algorithm constantly monitoring tweets and soon news.
Here's the link http://bitmood.net. We already analysing Bitcoin and Iota.
Feel free to give me negative feedback and suggestion, I would love to know if some of you would use it or have ideas about improvement that would make the website more usefull for you.
Thanks