Resources and Approaches for Fine-Grained Sentiment Analysis Tasks
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 838
Special Issue Editors
Interests: Evaluation and Development of Linguistic Resources; Syntax and morphology; Sentiment Analysis; Universal Dependencies; Hate Speech Detection; Irony Detection
Special Issue Information
Dear Colleagues,
We are witnessing a growing interest in computational approaches for addressing emotions, sentiment, and opinions, going beyond simple polarity or stance against a target, expressed in social media messages. In the last few years, tasks oriented toward a large variety of facets and implementing finer-grained perspectives were studied, by creating novel annotated resources and applying more efficient detection methodologies, but also observing at a finer texture the characteristics of the annotation steps (e.g., biases and agreement among the annotators).
On the one hand, the opportunity of capturing affective content in social media or in other kinds of textual genres mostly depends on our ability to understand the articulation of subjective experiences described in these contexts. On the other hand, by modeling experiences with computer-based affect analysis, we are forced to achieve a deeper understanding of the involved phenomena, and also to try to better encode the perception of people whose behavior and affectivity have to be modeled by machines. The articulation of affective experiences can involve, in particular, the inclusion of several annotation layers (and cues) for analysis in the resources, and the implementation of more specialized features and methodologies in the approaches. We are especially interested in investigating these aspects of both resources and approaches.
The main goal of this Special Issue is, therefore, to collect unpublished studies about specific typologies of fine-grained sentiment analysis, to broaden the scenario of resources and automatic tools created in recent years, and, to provide a deeper analysis of the specific phenomena involved.
The topics to be addressed in this Special Issue, therefore, include, but are not limited to the following:
- Detection of figurative language devices (irony and sarcasm, metaphor, etc.)
- Fine-grained detection of emotions and sentiment
- Detection of stance (towards specific targets, or open stance classification)
- Detection of rumors and fake news
- Detection and annotation of stereotypes against groups or individuals (based on religion, gender, sexual orientation, ethnicity, etc.)
- Misogyny detection and modeling
- Studies on demographic bias in NLP models for sentiment analysis
Dr. Cristina Bosco
Dr. Alessandra Teresa Cignarella
Guest Editors
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Keywords
- Sentiment analysis
- Affective computing
- Natural language processing
- Linguistic resources
- Irony detection
- Stance detection
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