Publications

Papers

Social Sentiment Indices powered by X-Scores

Abstract:

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.

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Venue:

The Second International Conference on Big Data, Small Data, Linked Data and Open Data

ALLDATA 2016

February 21 - 25, 2016 - Lisbon, Portugal

https://www.iaria.org/conferences2016/AwardsALLDATA16.html

 

Award:

Best Paper Award

 

 

 

In or Out? Real-Time Monitoring of BREXIT sentiment on Twitter

Abstract:

The SSIX (Social Sentiment analysis financial IndeXes) project is a European Innovation Project sponsored by the European Commission under the Horizon 2020 framework. SSIX aims to provide European SMEs with a collection of easy to interpret tools to analyse and understand social media sentiment for any given topic regardless of locale or language. The United Kingdom’s recent referendum on European Union membership i.e. staying (“Bremain”) or leaving the EU (“Brexit”) was selected for the initial real-world test case for the validating the SSIX methodology and platform. In this paper, we describe the SSIX architecture in brief as well as analysis of the platforms X-Scores metrics and their application to Brexit, our initial experimental results and lessons learned.
 

Venue: 

SEMANTiCS 2016 (poster & demo track)

http://alt.qcri.org/semeval2017/index.php

 

 

 

A Twitter Sentiment Gold Standard for the Brexit Referendum

Abstract:

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.

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Venue: 

SEMANTiCS 2016 (poster & demo track)

http://ceur-ws.org/Vol-1695/

http://alt.qcri.org/semeval2017/index.php

 

 

 

 

Semantic Relation Classification: Task Formalisation and Refinement (shortened version)

Abstract:

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.

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Venue:

Cogalex workshop @ COLING 2016 papers

http://coling2016.anlp.jp/#cfp

 

 

 

 

 

 

Fine-Grained Sentiment Analysis on Financial Microblogs and News

Abstract:

This paper discusses the “Fine-Grained Sentiment Analysis on Financial Mi- croblogs and News” task as part of SemEval-2017, specifically under the “Detecting sentiment, humour, and truth” theme. This task contains two tracks, where the first one concerns Microblog messages and the second one covers News Statements and Headlines. The main goal behind both tracks was to predict the sentiment score for each of the mentioned companies/stocks. The sentiment scores for each text instance adopted floating point values in the range of -1 (very negative/bearish) to 1 (very positive/bullish), with 0 designating neutral sentiment. This task attracted a total of 32 participants, with 25 participating in Track 1 and 29 in Track 2.

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Venue:

SemEval-2017 Shared task proposal

http://alt.qcri.org/semeval2017/index.php

Special Reports

BIG DATA MEETS POLITICS

Abstract:

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.

 

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