PhD position on ’machine learning methods for sentiment analysis and emotion detection’
|Workplace||Flemish Region, Ghent, Belgium|
PhD position on "machine learning methods for sentiment analysis and emotion detection"
Applications are invited for a fully-funded 4-year PhD scholarship within the Computational Web Intelligence (CWI) lab. CWI is a unit of the Fuzziness and Uncertainty Modelling (FUM) research group of the Department of Applied Mathematics, Computer Science and Statistics (TWIST) at Ghent University. The research will be conducted in cooperation with the Language and Translation Technology Team (LT3) at research group at Ghent University’s Department of Translation, Interpreting and Communication.
The scholarship forms part of the project "Fuzzy sets and rough sets for machine learning, sentiment analysis and web intelligence" funded by the Odysseus programme of the Research Foundation-Flanders. Fuzzy sets and rough sets are well-established mathematical AI models designed to deal with imperfections in data: on the one hand, fuzzy sets model vague information, by recognizing that membership to certain concepts, or logical truth of certain propositions, is a matter of degree. On the other hand, rough sets deal with incomplete information by approximating concepts from below and from above, using information granules as building blocks. In the project, we focus on the approximation of concepts using a fuzzy relation expressing degrees of similarity or dominance.
In particular, this PhD scholarship involves the development and evaluation of novel machine learning (ML) techniques to tackle the problems of sentiment analysis (SA) and emotion detection (ED) from textual data. The importance of these domains has increased manifold over the past years thanks to the availability of huge amounts of customer review data, and the relevance of the latter for e-commerce purposes. Your research will build on the results of previous research conducted at CWI and LT3, and will in particular combine fuzzy sets and rough sets with state-of-the-art ML techniques (deep learning, similarity learning, etc.) to address a variety of different tasks in SA/ED, involving challenging data settings such as semi-supervised, multi-label and ordinal classification.
Your key responsibilities include:
Profile of the candidate
We are especially interested in candidates with the following profile:
How to apply
The scholarship’s prospective starting date is October 1, 2019. Interested candidates should e-mail: (i) a detailed curriculum vitae; (ii) a motivation letter; (iii) BSc and MSc transcripts of grades and the MSc thesis, and (iv) two reference letters to Dr. Chris Cornelis ( Chris.Cornelis [at] UGent[.]be ) and Dr. Veronique Hoste ( Veronique.Hoste [at] UGent[.]be ) by May 31st, 2019. Indicate "Application: PhD Researcher on machine learning methods for sentiment analysis and emotion detection" in the email subject. Note that all applications will be thoroughly screened in order to satisfy the high scientific standards of our research group. The selection process will take place along June 2019, and will involve a small research task to assess your programming and problem solving skills and an interview (possible by Skype).
In your application, please refer to myScience.be
and reference JobID 1722.
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