A corpus for aspect-based sentiment analysis in Vietnamese

Nguyen, Minh-Hao; Nguyen, Tri Khoa; Thin, Dang Van; Nguyen, Ngan Luu-Thuy

doi:10.1109/kse.2019.8919448

Cited by 6 publications

(16 citation statements)

References 15 publications

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“…A GAN-SMOTE architecture was proposed by Scott and Plested [39] in which generative adversarial networks (GANs) was merged with SMOTE in order to up-sample their dataset and generate convincing synthetic examples of it for improving performance accuracy of their experiments. SMOTE technique was also applied by Nguyen et al [40] on restaurant data crawled from Foody, an online community people use to search and comment on food in Vietnam, to overcome the class imbalance challenge for supervised learning method to be applied afterward.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

et al. 2022

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Online media has an increasing presence on the restaurants' activities through social media websites, coinciding with an increase in customers' reviews of these restaurants. These reviews become the main source of information for both customers and decision-makers in this field. Any customer who is seeking such places will check their reviews first, which usually affect their final choice. In addition, customers' experiences can be enhanced by utilizing other customers' suggestions. Consequently, customers' reviews can influence the success of restaurant business since it is considered the final judgment of the overall quality of any restaurant. Thus, decision-makers need to analyze their customers' underlying sentiments in order to meet their expectations and improve the restaurants' services, in terms of food quality, ambiance, price range, and customer service. The number of reviews available for various products and services has dramatically increased these days and so has the need for automated methods to collect and analyze these reviews. Sentiment Analysis (SA) is a field of machine learning that helps analyze and predict the sentiments underlying these reviews. Usually, SA for customers' reviews face imbalanced datasets challenge, as the majority of these sentiments fall into supporters or resistors of the product or service. This work proposes a hybrid approach by combining the Support Vector Machine (SVM) algorithm with Particle Swarm Optimization (PSO) and different oversampling techniques to handle the imbalanced data problem. SVM is applied as a machine learning classification technique to predict the sentiments of reviews by optimizing the dataset, which contains different reviews of several restaurants in Jordan. Data were collected from Jeeran, a well-known social network for Arabic reviews. A PSO technique is used to optimize the weights of the features, as well as four different oversampling techniques, namely, the Synthetic Minority Oversampling Technique (SMOTE), SVM-SMOTE, Adaptive Synthetic Sampling (ADASYN) and borderline-SMOTE were examined to produce an optimized dataset and solve the imbalanced problem of the dataset. This study shows that the proposed PSO-SVM approach produces the best results compared to different classification techniques in terms of accuracy, F-measure, G-mean and AUC, for different versions of the datasets.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

et al. 2022

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“…For the Vietnamese language, there are a few research studies on the ABSA in recent years included public benchmark datasets (Nguyen et al [8], Thuy et al [24], Nguyen et al [25], Thuy et al [26]) and presented the methodology (Thin et al [27], Thin et al [28], Tran and Phan [29], Le et al [15]). To the best of our knowledge, the research of Nguyen et al [8] was the first study to publish benchmark datasets for the research community on the ABSA problem, which have same format of shared-task SemEval 2016 [6].…”

Section: Related Workmentioning

confidence: 99%

“…Next, Thuy et al [26] continually annotated the SPC task for this dataset and combined with translated English dataset [6] for the final dataset. Similarly, Nguyen et al [25] also presented a dataset at the document-level for ABSA with two tasks: ACD and SPC for the restaurant reviews. Compared with other datasets, their dataset was annotated with 7 aspect categories and 5 sentiment polarities.…”

Section: Related Workmentioning

confidence: 99%

“…The works of Thin et al [27], Thuy et al [24] and Thuy et al [26] also separate two tasks (ACD and SPC) as two components where each component consist of N binary classifiers corresponding to N aspect categories. In contrast, Nguyen et al [25] combined the two outputs of two tasks in a classifier for each aspect category. In our opinions, these approaches resolve aspect categories individually without utilizing relevant information between the aspect categories.…”

Section: Related Workmentioning

confidence: 99%

“…The output layer's size is the number of the entity, attribute, and aspect category classes for each domain. For the UIT_ABSA dataset [25], we only use the Entity as the output of this component because their dataset treated the Entity as the aspect category.…”

Section: A Component 1: Aspect Category Detectionmentioning

confidence: 99%

See 2 more Smart Citations

A Joint Multi-task Architecture for Document-level Aspect-based Sentiment Analysis in Vietnamese

2022

IJMLC

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The increasing demands of e-commerce websites have caused a vast amount of opinions about Internet users' products and services. Therefore, the aspect-based sentiment analysis (ABSA) has attracted much attention from academia and industry due to its application in many real-life problems in recent years. This problem is a complex task that aims to extract both the aspects and sentiments from the text input. Aspect Category Detection and Sentiment Polarity Classification are two of the challenging subtasks of ABSA, which detect a set of pre-defined categories and corresponding sentiment polarity for a given review. This paper presents an effective joint multi-task architecture based on neural network models to solve two tasks in the document-level ABSA datasets. Our model is designed to predict the whole mentioned aspect categories and corresponding sentiment polarities on the document-level datasets. We trained our model jointly on two tasks simultaneously and utilized the additional information of aspect category detection task for predicting the aspect categories and its sentiments for the specific domain. Our architecture can explore the hidden correlated information between categories and polarities in the review. Experiments on two Vietnamese language datasets in the restaurant domain and hotel domain demonstrated that our model outperforms the previous state of the art methods on two benchmark document-level dataset.

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SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence

Phan

Pham

Nguyen

et al. 2021

Knowledge Science, Engineering and Management

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A corpus for aspect-based sentiment analysis in Vietnamese

Cited by 6 publications

References 15 publications

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

A Joint Multi-task Architecture for Document-level Aspect-based Sentiment Analysis in Vietnamese

SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence

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