Social Sentiment Indices powered by X-Scores
Social Sentiment Indices powered by X-Scores (SSIX) seeks to address the challenge of extracting relevant and valuable economic signals in a cross-lingual fashion from the vast variety of and increasingly influential social media services; such as Twitter, Google+, Facebook, StockTwits and LinkedIn, and in conjunction with the most reliable and authoritative newswires, online newspapers, financial news networks, trade publications and blogs. A statistical framework of qualitative and quantitative parameters called X-Scores will power SSIX. This framework will interpret economically significant sentiment signals that are disseminated in the social ecosystem. Using X-Scores, SSIX will create commercially viable and exploitable social sentiment indices, regardless of language, locale and data format. SSIX and X-Scores will support research and investment decision making for European SMEs, enabling end users to analyse and leverage real-time social media sentiment data in their domain, creating innovative products and services to support revenue growth with focus on increased alpha generation for investment portfolios.
The Second International Conference on Big Data, Small Data, Linked Data and Open Data
February 21 - 25, 2016 - Lisbon, Portugal
Best Paper Award
In or Out? Real-Time Monitoring of BREXIT sentiment on Twitter
Venue: SEMANTiCS 2016 (poster & demo track)
A Twitter Sentiment Gold Standard for the Brexit Referendum
In this paper, we present a sentiment-annotated Twitter gold standard for the Brexit referendum. The data set consists of 2,000 Twitter messages (“tweets”) annotated with information about the sentiment expressed, the strength of the sentiment, and context dependence. This is a valuable resource for social media-based opinion mining in the context of political events.
SEMANTiCS 2016 (poster & demo track)
Semantic Relation Classification: Task Formalisation and Refinement (shortened version)
The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data. This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora. Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology. The resulting set of relations is well grounded, allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations.
Cogalex workshop @ COLING 2016 papers
Fine-Grained Sentiment Analysis on Financial Microblogs and News
Venue: SemEval-2017 Shared task proposal
BIG DATA MEETS POLITICS
Politicians across Europe often look suspiciously at the “big data” revolution as a trend imported from the US, which encroaches on their privacy. But others are also surfing the wave and see a multitude of areas where big data analytics can support decision-making – and sometimes also help politicians win an election.