Detection of Sarcasm and Nastiness: New Resources for Spanish Language

Justo, Raquel; Alcaide, José M.; Torres, M. Inés; Walker, Marilyn A.

doi:10.1007/s12559-018-9578-5

Cited by 17 publications

(12 citation statements)

References 40 publications

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“…Firstly, to allow for comparison with other literature in the field, we report the K-α for judgements on each attribute in Table 2. They range from 0.31 -0.39, which is comparable with other datasets labelling 'similar' phenomenon, such as sarcasm (0.24-0.38) (Swanson et al, 2014;Justo et al, 2018;D'Arcey et al, 2019), and hate speech with sub-attributes from Figure Eight annotators (0.21) (Lazaridou et al, 2020). The one exception is the set of judgements on whether a comment has a place in a healthy conversation, with a lower K-α of 0.26.…”

Section: The Ucc Datasetsupporting

confidence: 79%

Six Attributes of Unhealthy Conversations

Price¹,

Gifford-Moore²,

Flemming³

et al. 2020

Proceedings of the Fourth Workshop on Online Abuse and Harms

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We present a new dataset of approximately 44000 comments labeled by crowdworkers. Each comment is labelled as either 'healthy' or 'unhealthy', in addition to binary labels for the presence of six potentially 'unhealthy' sub-attributes: (1) hostile; (2) antagonistic, insulting, provocative or trolling; (3) dismissive; (4) condescending or patronising; (5) sarcastic; and/or (6) an unfair generalisation. Each label also has an associated confidence score. We argue that there is a need for datasets which enable research based on a broad notion of 'unhealthy online conversation'. We build this typology to encompass a substantial proportion of the individual comments which contribute to unhealthy online conversation. For some of these attributes, this is the first publicly available dataset of this scale. We explore the quality of the dataset, present some summary statistics and initial models to illustrate the utility of this data, and highlight limitations and directions for further research.

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Section: The Ucc Datasetsupporting

confidence: 79%

Six Attributes of Unhealthy Conversations

Price¹,

Gifford-Moore²,

Flemming³

et al. 2020

Proceedings of the Fourth Workshop on Online Abuse and Harms

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show abstract

“…They considered the gaze behavior of the readers to understand sarcasm and highlighted cognition cognizant techniques involving eyetracking as a promising approach for sarcasm detection. Justo et al [36] proposed SOFOCO "Spanish Online Forums Corpus", wherein authors extracted dialogic debates from online sources, and further annotated by crowdsourcing platform to perform automatic analysis of sarcasm and nastiness. Mehta et al [47] discussed that personality trait can be used as an input for sarcasm detection task.…”

Section: Other Approachesmentioning

confidence: 99%

CAT-BiGRU: Convolution and Attention with Bi-Directional Gated Recurrent Unit for Self-Deprecating Sarcasm Detection

Kamal

Abulaish

2021

Cogn Comput

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Sarcasm detection has been a well-studied problem for the computational linguistic researchers. However, research related to different categories of sarcasm has still not gained much attention. Self-Deprecating Sarcasm (SDS) is a special category of sarcasm in which users apply sarcasm over themselves, and it is extensively used in social media platforms, mainly as an advertising tool for the brand endorsement, product campaign, and digital marketing with an aim to increase the sales volume. In this paper, we present a deep learning approach for detecting SDS on Twitter. We propose a novel Convolution and Attention with Bi-directional Gated Recurrent Unit (CAT-BiGRU) model, which consists of an input, embedding, convolutional, Bi-directional Gated Recurrent Unit (BiGRU), and two attention layers. The convolutional layer extracts SDS-based syntactic and semantic features from the embedding layer, BiGRU layer retrieves contextual information from the extracted features in both preceding and succeeding directions, and attention layers are used to retrieve SDS-based comprehensive context representation from the input texts. Finally, sigmoid function is employed to classify the input texts as a self-deprecating or non-self-deprecating sarcasm. Experiments are conducted over seven Twitter datasets to evaluate the proposed (CAT-BiGRU) model using standard evaluation metrics. The experimental results are impressive and significantly better than many neural network-based baselines and state-of-the-art methods. In this paper, we have highlighted biologically inspired and psychologically motivated basis of the proposed approach to examine its affective capabilities with respect to SenticNet. The efficacy of the proposed model is evaluated on two SenticNet-based sentic computing resources-Amazon word embedding and AffectiveSpace. Based on the experimental results, we conclude that deep learning-based approaches have potential to detect SDS in social media texts accurately.

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“…Nevertheless, most resources available are for the English language-Justo et al [33] stated that the majority of research in disciplines like SA addresses English, even though 48% of Internet resources are written in other languages. This results in the need for creating resources in other languages as well [30,34].…”

Section: Related Workmentioning

confidence: 99%

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

et al. 2021

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Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field.

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Detection of Sarcasm and Nastiness: New Resources for Spanish Language

Cited by 17 publications

References 40 publications

Six Attributes of Unhealthy Conversations

Six Attributes of Unhealthy Conversations

CAT-BiGRU: Convolution and Attention with Bi-Directional Gated Recurrent Unit for Self-Deprecating Sarcasm Detection

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

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