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Robust Extraction of Metaphor from Novel Data
This article uses Topical Structure and Tracking, an Imageability score, and innovative methods to build an effective metaphor identification system that is fully automated and performs well over baseline.
Using Imageability and Topic Chaining to Locate Metaphors in Linguistic Corpora
A novel approach to metaphor identification which is based on three intersecting methods: imageability, topic chaining, and semantic clustering is described, which hypothesis is that metaphors are likely to use highly imageable words that do not generally have a topical or semantic association with the surrounding context.
Measuring Game Engagement
Background. Engagement has been identified as a crucial component of learning in games research. However, the conceptualization and operationalization of engagement vary widely in the literature.…
TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep Learning
TeamUNCC’s system to detect emotions in English and Arabic tweets is described and performance results show substantial improvements in Spearman correlation scores over the baseline models provided by Task 1 organizers.
ANEW+: Automatic Expansion and Validation of Affective Norms of Words Lexicons in Multiple Languages
The method of automatically expanding an existing lexicon of words with affective valence scores is described and the procedure for automatically creating lexicons in languages where such resources may not previously exist is described.
I Stand With You: Using Emojis to Study Solidarity in Crisis Events
Emojis are a powerful indicator of sociolinguistic behaviors that are exhibited on social media as the crisis events unfold and are used to characterize human behavior in online social networks, through the temporal and geospatial diffusion of emojis.
SEDAT: Sentiment and Emotion Detection in Arabic Text Using CNN-LSTM Deep Learning
- Malak Abdullah, M. Hadzikadic, Samira Shaikh
- Computer Science17th IEEE International Conference on Machine…
- 1 December 2018
SEDAT is described, to detect sentiments and emotions in Arabic tweets using word and document embeddings and a set of semantic features and applying CNN-LSTM and a fully connected neural network architectures to obtain performance results that show substantial improvements in Spearman correlation scores over the baseline models.
Can You Verifi This? Studying Uncertainty and Decision-Making About Misinformation Using Visual Analytics
It is revealed that the presence of conflicting information, presented to users in the form of cues, impacts the ability to judge the veracity of news in systematic ways and has the potential to inform the design of visual analytics systems so that they may be used to mitigate the effects of cognitive biases and stymie the spread of misinformation on social media.
MPC: A Multi-Party Chat Corpus for Modeling Social Phenomena in Discourse
- Samira Shaikh, T. Strzalkowski, G. Broadwell, Jennifer Stromer-Galley, Sarah M. Taylor, N. Webb
- Computer ScienceLREC
- 1 May 2010
This effort is part of a larger project to develop computational models of social phenomena such as agenda control, influence, and leadership in on-line interactions to help capturing the dialogue dynamics that are essential for developing realistic human-machine dialogue systems, including autonomous virtual chat agents.
Modeling Socio-Cultural Phenomena in Discourse
- T. Strzalkowski, G. Broadwell, Jennifer Stromer-Galley, Samira Shaikh, Sarah M. Taylor, N. Webb
- 23 August 2010
A two-tier approach is developed in which certain social language uses are first detected and classify that serve as first order models from which presence the higher level social constructs such as leadership, may be inferred.