Adversarial Training for Aspect-Based Sentiment Analysis with BERT

Karimi, Akbar; Rossi, Leonardo; Prati, Andrea

doi:10.1109/icpr48806.2021.9412167

Cited by 91 publications

(44 citation statements)

References 19 publications

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“…A core sub-task in this approach is Aspect-Based Sentiment Analysis: identification of aspect mentions in the text, which may be further classified into highlevel aspect categories, and classification of the sentiment towards these mentions. Recent examples are (Ma et al, 2019;Miao et al, 2020;Karimi et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Every Bite Is an Experience: Key Point Analysis of Business Reviews

Bar-Haim¹,

Eden²,

Kantor³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary. These approaches provide only a partial view of the data: aspect-based sentiment summaries lack sufficient explanation or justification for the aspect rating, while textual summaries do not quantify the significance of each element, and are not well-suited for representing conflicting views. Recently, Key Point Analysis (KPA) has been proposed as a summarization framework that provides both textual and quantitative summary of the main points in the data. We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata. We show empirically that these novel extensions of KPA substantially improve its performance. We demonstrate that promising results can be achieved without any domain-specific annotation, while human supervision can lead to further improvement.

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Section: Related Workmentioning

confidence: 99%

Every Bite Is an Experience: Key Point Analysis of Business Reviews

Bar-Haim¹,

Eden²,

Kantor³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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show abstract

“…Especially, the ADAN was trained with labelled source text data from English and unlabeled target text data from Arabic and Chinese. More recently, Karimi et al [160] fine-tuned the general-purpose BERT and domain-specific post-trained BERT using adversarial training, which showed promising results in TSA.…”

Section: 12mentioning

confidence: 99%

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances

Wang¹,

Song²,

Tao³

et al. 2022

Preprint

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Affective computing conjoins the research topics of emotion recognition and sentiment analysis, and can be realized with unimodal or multimodal data, consisting primarily of physical information (e.g., text, audio, and visual) and physiological signals (e.g., EEG and ECG). Physical-based affect recognition caters to more researchers due to the availability of multiple public databases, but it is challenging to reveal one's inner emotion hidden purposefully from facial expressions, audio tones, body gestures, etc. Physiological signals can generate more precise and reliable emotional results; yet, the difficulty in acquiring these signals hinders their practical application. Besides, by fusing physical information and physiological signals, useful features of emotional states can be obtained to enhance the performance of affective computing models. While existing reviews focus on one specific aspect of affective computing, we provide a systematical survey of important components: emotion models, databases, and recent advances. Firstly, we introduce two typical emotion models followed by five kinds of commonly used databases for affective computing. Next, we survey and taxonomize state-of-the-art unimodal affect recognition and multimodal affective analysis in terms of their detailed architectures and performances. Finally, we discuss some critical aspects of affective computing and its applications and conclude this review by pointing out some of the most promising future directions, such as the establishment of benchmark database and fusion strategies. The overarching goal of this systematic review is to help academic and industrial researchers understand the recent advances as well as new developments in this fast-paced, high-impact domain.

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“…They used AMDA to defend against attacks from PPWS [164] and TextFooler [139] on the data sets SST-2, AG News and IMBD, and achieved significant robustness gains in both Targeted Attack Evaluation (TAE) and Static Attack Evaluation (SAE). For large pre-training model BERT, Karimi et al [171] introduced a method named BAT to fine-tuned the BERT model by using normal and adversarial text at the same time to obtain a model with better robustness and generalization ability. The experiment indicated that the BERT model trained with BAT was more robust than the traditional BERT model in aspect-based sentiment analysis task.…”

Section: Model Robustness Enhancementmentioning

confidence: 99%

Adversarial Machine Learning on Social Network: A Survey

Guo

2021

Front. Phys.

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In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.

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Adversarial Training for Aspect-Based Sentiment Analysis with BERT

Cited by 91 publications

References 19 publications

Every Bite Is an Experience: Key Point Analysis of Business Reviews

Every Bite Is an Experience: Key Point Analysis of Business Reviews

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances

Adversarial Machine Learning on Social Network: A Survey

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