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Our new series labelled “DApp of the Week” regularly features the most innovative applications built on top of iExec, to showcase what can be achieved with the tools and librairies we have developed, and how you can already launch decentralized applications running on a decentralized cloud.
The DApp of the Week is BirdieBlue, and has been developed by Mircea Moca, Sergiu Nistor and Darie Moldovan.
BirdieBlue is a sentiment analysis application, which was one of the winners of the iExec DApp Challenge. The application uses a sophisticated method that performs sentiment analysis on crypto tweets. This is performed by an AI module running classifications on tweets.
BB identifies and extracts subjective information and opinions expressed in a piece of text, for the purpose of establishing the attitude of the writer in regards to a particular subject. And afterwards, the app categorizes that attitude as positive, negative or neutral — otherwise known as “sentiment analysis”.
The developers behind BirdieBlue made sure to provide the app with small pieces of text already classified, that gradually evolved from short, simple texts to texts that were slightly more complex or elaborated. With this coaching and support, the app first learns to discern between the simple positive / negative / neutral, and it’s mechanism is becoming more advanced every day.
BirdieBlue will gain, in time, the capacity to see subtleties in a text, to recognize sarcasm, to classify the nuances of “positive” or “negative” based on the relevance of the affirmation in the text: was it mildly negative or extremely negative, is the negative opinion related to a high-importance subject or it that topic random or even irrelevant?
In the initial setup, its feed is the incoming data from Twitter regarding Bitcoin, and will later be able to analyze tweets about other cryptocurrencies as well.
BirdieBlue collects data from Twitter and stores it in a data warehouse where it becomes available for further pre-processing. The extraction process consists in applying some predefined filters in order to make sure only relevant data is gathered.
The flow continues with a numerical transformation of the text. The algorithms works with numbers, not words. This means every word in a tweet [even emojis 🙂] will be coded into relevant numbers.
The above steps are part of pre-processing phase which, even if it doesn’t seem spectacular, is critical for the success of the entire process. Did you know that in data scientists’ work about 80% of their time is consumed preparing and managing data for the analysis?
Once the data preprocessing is finished, the actual analysis takes place. Each tweet is processed through the Recurrent Neural Network, where the sentiments are extracted. The Network makes a decision on the relevant words in a tweet based on previous experiences from which it learned.
Old or new, the rules discovered are kept in a Lexicon, which is a dictionary that keeps in memory all the knowledge regarding sentiments classification.
Based on several factors (e.g. count, volumes of tweets), the app computes the intensity of sentiments (positive and negative) and provides it to the user, taking into account a certain time frame.
The time frame is an important feature when analyzing the sentiment (as it is when analyzing financial data). A sudden change in sentiment intensity can be the result of a disruptive news release, but one should also take a look at the big picture to check the persistence of this change and compare it to previous values.
Today, BirdieBlue uses iExec to perform sentiment classifications on Bitcoin tweets. Once GPU support is enabled, the application will also utilize iExec to train AI models, and gain extremely valuable time compared to running these computations on a centralized solution.
To watch BirdieBlue at work or follow the projects of SynergyCrowds check the website www.synergycrowds.io!
SynergyCrowds is an ambitious software technology project building a system for decentralized knowledge production. This is an Ethereum blockchain-based global system, where knowledge is produced by crowds and cutting-edge computational methods of data science and analytics.
SynergyCrowds system creates specific mechanisms that allow crowd members to be organized and profiled based on their contribution and facilitates their access to different applications within the SynergyCrowds system, allowing them to produce knowledge or perform professional actions.
Mircea Moca, CEO and co-Founder of SynergyCrowds, answered our questions, and discussed his favorite features from iExec and the process of deploying applications on a decentralized cloud.
Sources
[1] Mircea Moca (2018) What is SynergyCrowds
[2] Miruna Morosanu (2018) BirdieBlue — not an ordinary blue bird
[3] Darie Moldovan (2018) What is BirdieBlue?
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