SSIX - Social Sentiment Indices powered by X-Scores

One year ago people all over the world were still trying to understand the political earthquake of the Brexit-Votum and a few months later Donald Trump was elected as US president. At that time, the journalists Hannes Grassegger and Mikael Krogerus published an article in the Swish magazine “Das Magazin”, which immediately went viral in… » read more

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Following on our previous article from December 16th, 2016. We now discuss the use of a social sentiment data for investment and training. The strategies and algorithms we describe here are simplified and are not intended for live trading but just for demonstration purposes. A live trading algorithm requires many more rules and functions to… » read more

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Investors daily have received news and opinions about different stocks or firms through specialised platforms. These one convey information, which leads different investors to make decisions. These decisions impact the market, which aggregates the information that investors receive and reflects it via the price of the firms or stocks. Given an algorithm for classifying text… » read more

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The SSIX project targets sentiment analysis in the financial domain. One of its main objectives is to overcome language barriers and realize a financial sentiment platform capable of scoring textual data in different languages. The three-year project was launched with English. In Year 2, we developed an English Gold Standard (GS) corpus and trained an… » read more

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In the recent blog post “Employing Social Sentiment Data for Investment and Trading: An Introduction (Part 1)”, we’ve explained that paying attention to the sentiment in social networks can be of great value for traders and investors. Let’s now turn our attention towards those people who observe the world of finance and report about it:… » read more

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The creation of real-world Artificial Intelligence (AI) applications is dependent on leveraging a large volume of commonsense knowledge. Simple semantic interpretation tasks such as understanding that if ‘A is married to B’ then ‘A is the spouse of B’ or that ‘car, vehicle, auto’ have very similar meanings are examples of semantic approximation operations/inferences that… » read more

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Following the multilingual approach for Sentiment Analysis described previously, today the SSIX Project is pleased to announce the public release of two important pieces of software in our machine translation chain. We are opensourcing two new clients to programmatically access some features of the GeoFluent API: GeoFluent Java Client GeoFluent Python Client Both software components… » read more

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This article is the first part from a short series of articles about investment and trading using SSIX social sentiment data. As social networks become more and more part of our daily life, an increasing volume of comments and opinions covering a huge variety of topics are shared every second. Considering that social networks are… » read more

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Posted by ssix