
Cynthia Van HeeGhent University | UGhent · Department of Translation, Interpreting and Communication
Cynthia Van Hee
PhD in Linguistics
About
28
Publications
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Introduction
Cynthia Van Hee is a post-doctoral researcher at LT3 at Ghent University, active in the field of computational linguistics and machine learning. In the framework of her PhD, she created a theoretic framework of irony and developed a state-of-the-art irony detection system using prototypical sentiment information. Her other research interests include sentiment and emotion analysis and cyberbullying detection, which was one of the use cases of the AMiCA project in which she was actively involved.
Additional affiliations
September 2013 - present
Education
September 2009 - June 2013
Publications
Publications (28)
In this paper, we explore the feasibility of irony detection in Dutch social media. To this end, we investigate both transformer models with embedding representations, as well as traditional machine learning classifiers with extensive feature sets. Our feature-based methodology implements a variety of information sources including lexical, semantic...
In this paper, we explore the feasibility of irony detection in Dutch social media. To this end, we investigate both transformer models with embedding representations, as well as traditional machine learning classifiers with extensive feature sets. Our feature-based methodology implements a variety of information sources including lexical, semantic...
Background
Headache medicine is largely based on detailed history taking by physicians analysing patients’ descriptions of headache. Natural language processing (NLP) structures and processes linguistic data into quantifiable units. In this study, we apply these digital techniques on self-reported narratives by patients with headache disorders to r...
Since the rise of social media, the authority of traditional professional literary critics has beensupplemented – or undermined, depending on the point of view – by technological developmentsand the emergence of community-driven online layperson literary criticism. So far, relatively littleresearch (Allington 2016, Kellermann et al. 2016, Kellerman...
The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data. Nevertheless, while computational power and affordability of resources continue to increase, the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of th...
Waar taal en technologie samenkomen, ontstaat het domein van Natural Language Processing (NLP). Met technieken uit machine learning, zoals het trainen van modellen om tekst, afbeeldingen of geluidsfrequenties te herkennen, kunnen we computers op een intelligente manier laten werken met taal. Bekende NLP-toepassingen van machine learning zijn bijvoo...
Successful prevention of cyberbullying depends on the adequate detection of harmful messages. Given the impossibility of human moderation on the Social Web, intelligent systems are required to identify clues of cyberbullying automatically. Much work on cyberbullying detection focuses on detecting abusive language without analyzing the severity of t...
Concerns about selective exposure and filter bubbles in the digital news environment trigger questions regarding how news recommender systems can become more citizen-oriented and facilitate – rather than limit – normative aims of journalism. Accordingly, this chapter presents building blocks for the construction of such a news algorithm as they are...
The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data. Nevertheless, while computational power and affordability of resources continue to increase, the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of th...
While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overlo...
Although common sense and connotative knowledge come naturally to most people, computers still struggle to perform well on tasks for which such extratextual information is required. Automatic approaches to sentiment analysis and irony detection have revealed that the lack of such world knowledge undermines classification performance. In this articl...
To push the state of the art in text mining applications, research in natural language processing has increasingly been investigating automatic irony detection, but manually annotated irony corpora are scarce. We present the construction of a manually annotated irony corpus based on a fine-grained annotation scheme for irony that allows to identify...
While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overlo...
In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvest...
CLIN27 conference poster with intermediate results on cyberbullying detectection in the AMiCA project.
Social media provide an increasingly used platform for crisis communication. Governments need to understand how publics consume and react to crisis information via social media. One option to do this is by applying emotion analysis. In this pilot study, we target the November 2015 terrorist attacks in Paris as a case study for emotion analysis and...
Social media, and in particular Twitter, are increasingly being utilized during crises. It has been shown that tweets offer valuable real-time information for decision-making. Given the vast amount of data available on the Web, there is a need for intelligent ways to select and retrieve the desired information. Analyzing sentiment and emotions in o...
Handling figurative language like irony is currently a challenging task in natural language processing. Since irony is commonly used in user-generated content, its presence can significantly undermine accurate analysis of opinions and sentiment in such texts. Understanding irony is therefore important if we want to push the state-of-the-art in task...
The recent development of social media poses new challenges to the research community in analyzing online interactions between people. Social networking sites offer great opportunities for connecting with others, but also increase the vulnerability of young people to undesirable phenomena, such as cybervictimization. Recent research reports that on...
In the current era of online interactions, both positive and negative experiences are abundant on the Web. As in real life, negative experiences can have a serious impact on youngsters. Recent studies have reported cybervictimization rates among teenagers that vary between 20% and 40%. In this paper, we focus on cyberbullying as a particular form o...
This paper describes our contribution to the SemEval-2014 Task 9 on sentiment analysis in Twitter. We participated in both strands of the task, viz. classification at message-level (subtask B), and polarity disambiguation of particular text spans within a message (subtask A). Our experiments with a variety of lexical and syntactic features show tha...