Identifying outlier opinions in an online intelligent argumentation system

Arvapally, Ravi Santosh; Liu, Xiaoqing Frank; Nah, Fiona Fui‐Hoon; Jiang, Wei

doi:10.1002/cpe.4107

Cited by 5 publications

(6 citation statements)

References 43 publications

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“…Our research group has developed an intelligent cyber argumentation system, ICAS, for facilitating large scale discussions among many users (Liu et al, 2007(Liu et al, , 2010(Liu et al, , 2011Chanda and Liu, 2015;Liu et al, 2012;Arvapally et al, 2017;Sirrianni et al, 2018). ICAS an updated version of the OLIAS argumentation system (Arvapally and Liu, 2013).…”

Section: Icas Platformmentioning

confidence: 99%

“…Research in Cyber Argumentation has shown that incorporating both stance polarity and intensity information into online discussions improves the analysis of discussions and the various phenomena that arise during a debate, including opinion polarization (Sirrianni et al, 2018), and identifying outlier opinions (Arvapally et al, 2017), compared to using stance polarity alone. Thus, automatically identifying both the post's stance polarity and intensity, allows these powerful analytical models to be applied to unstructured debate data from platforms such as Twitter, Facebook, Wikipedia, comment threads, and online forums.…”

Section: Introductionmentioning

confidence: 99%

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Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity

Sirrianni¹,

Liu²,

Adams³

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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In online debates, users express different levels of agreement/disagreement with one another's arguments and ideas. Often levels of agreement/disagreement are implicit in the text and must be predicted to analyze collective opinions. Existing stance detection methods predict the polarity of a post's stance toward a topic or post, but don't consider the stance's degree of intensity. We introduce a new research problem, stance polarity and intensity prediction in response relationships between posts. This problem is challenging because differences in stance intensity are often subtle and require nuanced language understanding. Cyber argumentation research has shown that incorporating both stance polarity and intensity data in online debates leads to better discussion analysis. We explore five different learning models: Ridge-M regression, Ridge-S regression, SVR-RF-R, pkudblab-PIP, and T-PAN-PIP for predicting stance polarity and intensity in argumentation. These models are evaluated using a new dataset for stance polarity and intensity prediction collected using a cyber argumentation platform. The SVR-RF-R model performs best for prediction of stance polarity with an accuracy of 70.43% and intensity with RMSE of 0.596. This work is the first to train models for predicting a post's stance polarity and intensity in one combined value in cyber argumentation with reasonably good accuracy.

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Section: Icas Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity

Sirrianni¹,

Liu²,

Adams³

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…Those data points that are far from their respective neighbors are considered as outliers. Density‐based methods use more complex mechanisms to model the outlier‐ness of data points than distance‐based methods . They usually involve investigating not only the local density of the data being studied but also the local densities of its nearest neighbors.…”

Section: Related Workmentioning

confidence: 99%

“…Density-based methods use more complex mechanisms to model the outlier-ness of data points than distance-based methods. [16][17][18][19][20][21][22] They usually involve investigating not only the local density of the data being studied but also the local densities of its nearest neighbors. Because of the close relationships between data clusters and outliers, clustering analysis can also be performed to assist the detection of outliers by defining outliers as data that do not lie in or located far apart from any clusters.…”

Section: Related Workmentioning

confidence: 99%

Large dataset summarization with automatic parameter optimization and parallel processing for local outlier detection

Shou

2018

Concurrency and Computation

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Summary As one of the most important research problems of data analytics and data mining, outlier detection from large datasets has drawn many research attentions in recent years. In this paper, we investigate the interesting research problem of summarizing large datasets for supporting efficient local outlier detection. To summarize large datasets, efficient summarization algorithms are proposed that produce a highly compact summary of the original dataset, which can be applied to detect local outliers from future similar datasets. A novel automatic parameter optimization method is proposed to produce the optimal setup of the key parameters used in the summarization algorithm. Parallel processing methods are also proposed to accelerate significantly the summarization process. The experimental evaluation results demonstrate that our proposed algorithms are highly scalable for large datasets and effective in producing dataset summary for local outlier detection.

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“…Even though classifying a more granular stance polarity is harder, a study on Cyber Argumentation shows that using stance polarity with the intensity of the relationship can improve the results of discussion analysis, such as opinion polarization (Sirrianni et al , 2018) and identifying outlier opinions (Arvapally et al , 2017), compared to analysis using only stance polarity. Therefore, measuring the intensity of the argument is still necessary.…”

Section: Introductionmentioning

confidence: 99%

CNN-BERT for measuring agreement between argument in online discussion

Harly

Girsang

2022

IJWIS

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Purpose With the rise of online discussion and argument mining, methods that are able to analyze arguments become increasingly important. A recent study proposed the usage of agreement between arguments to represent both stance polarity and intensity, two important aspects in analyzing arguments. However, this study primarily focused on finetuning bidirectional encoder representations from transformer (BERT) model. The purpose of this paper is to propose convolutional neural network (CNN)-BERT architecture to improve the previous method. Design/methodology/approach The used CNN-BERT architecture in this paper directly uses the generated hidden representation from BERT. This allows for better use of the pretrained BERT model and makes finetuning the pretrained BERT model optional. The authors then compared the CNN-BERT architecture with the method proposed in the previous study (BERT and Siamese-BERT). Findings Experiment results demonstrate that the proposed CNN-BERT is able to achieve a 71.87% accuracy in measuring agreement between arguments. Compared to the previous study that achieve an accuracy of 68.58%, the CNN-BERT architecture was able to increase the accuracy by 3.29%. The CNN-BERT architecture is also able to achieve a similar result even without further pretraining the BERT model. Originality/value The principal originality of this paper is the proposition of using CNN-BERT to better use the pretrained BERT model for measuring agreement between arguments. The proposed method is able to improve performance and also able to achieve a similar result without further training the BERT model. This allows separation of the BERT model from the CNN classifier, which significantly reduces the model size and allows the usage of the same pretrained BERT model for other problems that also did not need to finetune their BERT model.

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Identifying outlier opinions in an online intelligent argumentation system

Cited by 5 publications

References 43 publications

Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity

Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity

Large dataset summarization with automatic parameter optimization and parallel processing for local outlier detection

CNN-BERT for measuring agreement between argument in online discussion

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