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Found 15 papers, 3 papers with code
Analyzing social media with crowdsourcing in Crowd4SDG
However, finding relevant information among millions of posts being posted every day can be difficult, and developing a data analysis project usually requires time and technical skills.
A Technical Survey on Statistical Modelling and Design Methods for Crowdsourcing Quality Control
Our survey is the first to bridge the two branches by providing technical details on how they work together under frameworks that systematically unify crowdsourcing aspects modelled by both of them to determine the response quality.
CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities
To match the implicit assumption, we propose to transform a given dataset such that the transformed clusters have approximately the same density while all regions of locally low density become globally low density -- homogenising cluster density while preserving the cluster structure of the dataset.
Distinguishing Question Subjectivity from Difficulty for Improved Crowdsourcing
Experiments show that our model(1) improves the performance of both quality control for crowd-sourced answers and next answer prediction for crowd-workers, and (2) can potentially provide coherent rankings of questions in terms of their difficulty and subjectivity, so that task providers can refine their designs of the crowdsourcing tasks, e. g. by removing highly subjective questions or inappropriately difficult questions.
Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection
However, since the exact word where such an incongruity occurs may not be known in advance, we present two approaches: an All-words approach (which consults sentence completion for every content word) and an Incongruous words-only approach (which consults sentence completion for the 50% most incongruous content words).
Efficient Benchmarking of NLP APIs using Multi-armed Bandits
Comparing NLP systems to select the best one for a task of interest, such as named entity recognition, is critical for practitioners and researchers.
`Who would have thought of that!': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection
Designed on the basis of the intuition that sarcastic tweets are likely to have a mixture of words of both sentiments as against tweets with literal sentiment (either positive or negative), our hierarchical topic model discovers sarcasm-prevalent topics and topic-level sentiment.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Automatic Identification of Sarcasm Target: An Introductory Approach
To compare our approach, we use two baselines: a na\"ive baseline and another baseline based on work in sentiment target identification.
Are Word Embedding-based Features Useful for Sarcasm Detection?
For example, this augmentation results in an improvement in F-score of around 4\% for three out of these four feature sets, and a minor degradation in case of the fourth, when Word2Vec embeddings are used.
A Computational Approach to Automatic Prediction of Drunk Texting
Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks.
