Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews

Al-Ghuribi, Sumaia Mohammed; Noah, Shahrul Azman Mohd; Tiun, Sabrina

doi:10.1109/access.2020.3042312

Cited by 34 publications

(17 citation statements)

References 75 publications

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“…As [6] also claimed, a practical strategy would be to enhance the generalization of developed English methods. Methods such as unsupervised learning have replaced reliance on labeled data [100]. Future works could consider a careful optimization of hyper-parameters to see how they impact the performance of the method [49,48,33].…”

Section: Discussion and Future Workmentioning

confidence: 99%

Twitter Hate Speech Detection: A Systematic Review of Methods, Taxonomy Analysis, Challenges, and Opportunities

2023

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Hate speech detection has substantially increased interest among researchers in the domain of natural language processing (NLP) and text mining. The number of studies on this topic has been growing dramatically. Thus, the purpose of this analysis is to develop a resource that consists of an outline of the approaches, methods, and techniques employed to address the issue of Twitter hate speech. This study can be used to aid researchers in the development of a more effective model for future studies. This review focused on studies published over the past eight years, i.e., from 2015 to 2022. This systematic search was carried out in December 2020 and updated in July 2022. Ninety-one articles published within the mentioned period met the set criteria and were selected for this review. From the evaluation of these works, it is clear that a perfect solution has yet to be found. To conclude, this paper focused on presenting an in-depth understanding of current perspectives and highlighted research opportunities to boost the quality of hate speech detection systems. In turn, this helps social networking services that seek to detect hate messages generated by users before they are posted, thus reducing the risk of targeted harassment.

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Section: Discussion and Future Workmentioning

confidence: 99%

Twitter Hate Speech Detection: A Systematic Review of Methods, Taxonomy Analysis, Challenges, and Opportunities

2023

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“…Additionally, the approaches can be classified into supervised methods and unsupervised methods. Unsupervised techniques to extract aspects are rule-based, frequency-based, and statistical methods [27] [28]. Rule-based approaches are based on predefined rules to extract aspect terms manually [29][30] or automatically [31] [32].…”

Section: A English and Other Languagesmentioning

confidence: 99%

Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

2022

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The exponential growth of the internet and a multi-fold increase in social media users in the last decade have resulted in a massive growth of unstructured data. Aspect-Based Sentiment Analysis (ABSA) is challenging because it performs a fine-grain analysis; it is a text analysis technique where the opinions group is based on the aspect. The Aspect Extraction (AE) task is one of the core subtasks of ABSA; it helps to identify aspect terms in the text, comments, or reviews. The challenge of the Arabic AE task increases due to the complexity of the Arabic language. This work aims to develop the Arabic AE task by proposing transfer learning using State-of-art pre-trained contextual language models. We concatenate the Bidirectional Encoder Representation from Transformers (BERT) language model and contextualize string embedding (Flair embedding) as a stacked embeddings layer for better word representation for Arabic language. Then, we extend it with different deep learning network architectures. For Arabic AE, the model is developed by concatenating the Arabic contextual language model, AraBERT, and Flair embedding as a contextual stacked embeddings layer with an extended layer, BiLSTM-CRF or BiGRU-CRF, for sequence labeling. Our proposed models are called BF-BiLSTM-CRF and BF-BiGRU-CRF. The proposed model is evaluated using the Arabic Hotel's reviews dataset. For performance evaluation, we used the F1 score. The experimental results show that the proposed BF-BiLSTM-CRF configuration outperformed the baseline and other models by achieving an F1score of 79.7%.

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“…ALSC can be performed using three main approaches: unsupervised, semi-supervised, and supervised. The unsupervised and semi supervised techniques mainly follow corpus based and lexicon-based approaches [14]. The corpus-based approach utilizes the domain specific large corpora for generating relevant information, which requires substantial manual effort, extensive training, and big data.…”

Section: Recent Trends In Aspect Level Sentiment Classificationmentioning

confidence: 99%

Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification

Sharma

Kaur

2021

Applied Sciences

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With the advancements in processing units and easy availability of cloud-based GPU servers, many deep learning-based methods have been proposed for Aspect Level Sentiment Classification (ALSC) literature. With this increase in the number of deep learning methods proposed in ALSC literature, it has become difficult to ascertain the performance difference of one method over the other. To this end, our study provides a statistical comparison of the performance of 35 recent deep learning methods with respect to three performance metrics-Accuracy, Macro F1 score, and Time. The methods are evaluated for eight benchmark datasets. In this study, the statistical comparison is based on Friedman, Nemenyi, and Wilcoxon tests. As per the results of statistical tests, the top-ranking methods could not significantly outperform several other methods in terms of Accuracy and Macro F1 score and performed poorly on-time metric. However, the time taken by any method is crucial to analyze the overall performance. Thus, this study aids the selection of the Deep Learning method, which maximizes the accuracy and Macro F1 score and takes minimal time. Our study also establishes a framework for validating the performance of new and alternate methods in ALSC that can be helpful for researchers and practitioners working in this area.

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Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews

Cited by 34 publications

References 75 publications

Twitter Hate Speech Detection: A Systematic Review of Methods, Taxonomy Analysis, Challenges, and Opportunities

Twitter Hate Speech Detection: A Systematic Review of Methods, Taxonomy Analysis, Challenges, and Opportunities

Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification

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