Browsing Data Science Institute by Author "Zarrouk, Manel"

Now showing items 1-13 of 13

    • Do city dashboards make sense? Conceptualising user experiences and challenges in using city dashboards. A case study 

      Vornhagen, Heike; Zarrouk, Manel; Davis, Brian; Young, Karen (Association for Computing Machinery (ACM), 2021-06-09)
      City dashboards present information about a city to a broad audience with some thought given as to how some of these audiences might understand the information. However, little research has looked at how ’citizens’ make ...

    • FinSentiA: sentiment analysis in English financial microblogs 

      Gaillat, Thomas; Sousa, Annanda; Zarrouk, Manel; Davis, Brian (NUI Galway, 2018-05-14)
      The objective of this paper is to report on the building of a Sentiment Analysis (SA) system dedicated to financial microblogs in English. The purpose of our work is to build a financial classifier that predicts the sentiment ...

    • From simplified text to knowledge representation using controlled natural language 

      Safwat, Hazem; Zarrouk, Manel; Davis, Brian (2017-04-17)
      Knowledge based systems provide means to store data and perform reasoning on top of it. Controlled Natural Language (CNL) is considered as an engineered subset of natural language. CNLs aim to abstract the complexity ...

    • Implicit and explicit aspect extraction in financial microblogs 

      Gaillat, Thomas; Stearns, Bernardo; McDermott, Ross; Sridhar, Gopal; Zarrouk, Manel; Davis, Brian (Association for Computational Linguistics, 2018-07-15)
      This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment ...

    • In or out? Real-time monitoring of BREXIT sentiment on Twitter 

      Vasiliu, Laurentiu; Freitas, André; Caroli, Frederico; McDermott, Ross; Zarrouk, Manel; Hürlimann, Manuela; Davis, Brian; Daudert, Tobias; Khaled, Malek Ben; Byrne, David; Fernández, Sergio; Cavallini, Angelo (CEUR Workshop Proceedings, 2016-09-12)
      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 ...

    • Multilingual multimodal machine translation for Dravidian languages utilizing phonetic transcription 

      Chakravarthi, Bharathi Raja; Priyadharshini, Ruba; Stearns, Bernardo; Jayapal, Arun; Sridevy, S.; Arcan, Mihael; Zarrouk, Manel; McCrae, John P. (European Association for Machine Translation, 2019-08-19)
      Multimodal machine translation is the task of translating from a source text into the target language using information from other modalities. Existing multimodal datasets have been restricted to only highly resourced ...

    • Rewriting simplified text into a controlled natural language 

      Safwat, Hazem; Zarrouk, Manel; Davis, Brian (IOS Press, 2018-08-27)
      While machine processable Controlled Natural Languages (CNLs) as a natural language interface have proven a popular, unambiguous and user friendly method for non experts to engineer formal knowledge-bases, human-oriented ...

    • SemEval-2017 Task 5: Fine-grained sentiment analysis on financial microblogs and news 

      Cortis, Keith; Freitas, André; Daudert, Tobias; Huerlimann, Manuela; Zarrouk, Manel; Handschuh, Siegfried; Davis, Brian (Association for Computational Linguistics, 2017-08-03)
      This paper discusses the “Fine-Grained Sentiment Analysis on Financial Microblogs and News” task as part of SemEval-2017, specifically under the “Detecting sentiment, humour, and truth” theme. This task contains two ...

    • SemR-11: a multi-lingual gold-standard for semantic similarity and relatedness for eleven languages 

      Barzegar, Siamak; Davis, Brian; Zarrouk, Manel; Handschuh, Siegfried; Freitas, André (European Language Resources Association, 2018-05-07)
      This work describes SemR-11, a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages (German, French, Russian, Italian, Dutch, Chinese, Portuguese, Swedish, Spanish, Arabic and Persian). ...

    • Sensemaking of complex sociotechnical systems: the case of governance dashboards 

      Vornhagen, Heike; Davis, Brian; Zarrouk, Manel (Association for Computing Machinery (ACM), 2018-05-30)
      This research project is concerned with developing a suitable visualization model to depict a complex socio-technical system such as a city. It focuses on governance dashboards as the main starting point as these aim to ...

    • The SSIX corpora: three gold standard corpora for sentiment analysis in English, Spanish and German financial microblogs 

      Gaillat, Thomas; Zarrouk, Manel; Freitas, André; Davis, Brian (European Language Resources Association, 2018-05-07)
      This paper introduces the three SSIX corpora for sentiment analysis. These corpora address the need to provide annotated data for supervised learning methods. They focus on stock-market related messages extracted from two ...

    • Understanding my city through dashboards. How hard can it be? 

      Vornhagen, Heike; Young, Karen; Zarrouk, Manel (International Federation for Information Processing (IFIP) and eJournal of eDemocracy and Open Government (JeDEM), 2019-09-02)
      This paper describes research into how current city dashboards support users' sensemaking processes. It uses criteria identified in previous research concerning visualisation and applies these to a number of city dashboards ...

    • WWW'18 open challenge: financial opinion mining and question answering 

      Maia, Macedo; Handschuh, Siegfried; Freitas, André; Davis, Brian; McDermott, Ross; Zarrouk, Manel; Balahur, Alexandra (Association for Computing Machinery, 2018-04-23)
      The growing maturity of Natural Language Processing (NLP) techniques and resources is dramatically changing the landscape of many application domains which are dependent on the analysis of unstructured data at scale. The ...