Sharpe Capital Improves The Use of Natural Language Processing (NLP)
The emergence of crowdsourcing has created a variety of new opportunities for improving traditional methods used for data collection and annotation. This, in turn, has created new opportunities for data-driven machine learning. Convenient access to crowd workers for simple data collection has resulted in leveraging more arbitrary crowd-based human computation for supplementing automated machine learning. Due to crowdsourcing, labelled data is now available in abundance which has proved to be a boon to data-driven machine learning. Crowd Sentiment has reduced traditional barriers to data collection which formerly encouraged several researchers to reuse existing data rather than collect and annotate their own. The Crowd is thereby changing the landscape for the quantity, quality, and type of labelled data available for training data-driven machine learning systems.
Crowd information is important information for understanding market feedback on certain commodities and services. However, to accurately analyze those reviews is a challenging task due to complications arising in natural language processing in reviews. Existing methods in machine learning only focus on studying efficient algorithms, but they cannot guarantee accuracy of review analysis. “The Crowd” can improve the accuracy of natural language processing techniques.
Sharpe Capital’s machine learning algorithms are collectively used to pre-process review information. Then reviews are selected on which all machine learning algorithms cannot agree and assign them to humans to process. In the final stage, results from machine learning and crowdsourcing are aggregated to generate the final analysis result. Thus, valuable information for understanding customers’ evaluations can be extracted through data analysis.
One of the most obvious benefits of crowd sentiment is that it has the ability to coordinate the distribution and validation of tasks. Data classified through crowdsourcing is being fed into computers to improve machine learning so that computers can learn to recognize images or words almost as well as we do. This has helped in maximizing the efficiency of machine learning to great extent.
The Sharpe Capital Investment Platform brings together a multitude of novel innovations in smart contracts, quantitative trading, machine learning, linguistic analysis and artificial intelligence. They are issuing Sharpe Platform Tokens (SHP). SHP provides a proof-of-stake that permits platform participants to earn service fees in ETH in exchange for providing sentiment toward global equities and blockchain assets through their web and mobile platforms.
The proof-of-stake metric allows us to infer the level of confidence that platform participants have in the sentiment they provide, which, when coupled with an immutable proof-of-reputation stored on the Ethereum blockchain, permits weighting of sentiment to determine both the size of service fees paid to each user, and the level of confidence to place upon each sentiment indication received.
Sharpe Capital’s Machine Learning modelling is complemented by advanced NLP algorithms and analysis of current trends. Sharpe Capital integrates with social media channels and popular online media outlets, constantly monitoring market sentiment in real-time.
