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

Phan, Luong Luc; Pham, Phuc Huynh; Nguyen, Kim Thi-Thanh; Huynh, Sieu Khai; Nguyen, Tham Thi; Nguyễn, Luân; Huynh, Tin Van; Nguyen, Kiet Van

doi:10.1007/978-3-030-82147-0_53

Cited by 10 publications

(11 citation statements)

References 22 publications

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“…The experimental results concluded that the PhoBERT model combined with the multi-task approach outperformed the machine learning and deep learning architectures and the single approach. Phan et al [14] provided a UIT-ViSFD (Vietnamese Smartphone Feedback Dataset) dataset consisting of 11,122 labelled mobile e-commerce comments. They also used this dataset to test the ABSA approach based on the Bi-LSTM model.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we evaluate the performance of our approach based on three benchmark datasets for different domains, including restaurant [6], smartphone [14] and document-level restaurant [16]. The general information for all the datasets is reported in Table 4, while more detailed textual statistics are available in the corresponding original works.…”

Section: Datasetsmentioning

confidence: 99%

“…Following the VLSP workshop [1], we report the micro Precision, Recall, and F1-score of all models over the test dataset of the restaurant domain. For the smartphone domain, we used the macro F1-score as previous studies [14,20] to compare the results. A prediction is correct if all predicted aspect categories and corresponding sentiments are correct.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The output sequence length of the two datasets were 128 and 156 for sentence-level and document-level data, respectively. For the smartphone domain [14], we set the maximum input sequence length to 256 since the training data contains samples with a maximum length of 250 tokens, and the output sequence length is 128. We train the models for all experiments with a batch size of 16, 8, 4, depending on the available computational resources.…”

Section: Settingsmentioning

confidence: 99%

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Aspect-Category based Sentiment Analysis with Unified Sequence-To-Sequence Transfer Transformers

Thin¹,

Nguyen²

2023

JCSCE

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In recent years, Aspect-based sentiment analysis (ABSA) has received increasing attention from the scientific community for Vietnamese language. However, most previous studies solved various subtasks in ABSA based on machine learning, deep learning and transformer-based architectures in a classification way. Recently, the release of pre-trained sequence-to-sequence brings a new approach to address the ABSA as a text generation problem for Vietnamese ABSA tasks. In this paper, we formulate the Aspect-category based sentiment analysis task as the conditional text generation task and investigate different unified generative transformer-based models. To represent the labels in a natural sentence, we apply a simple statistical method and observation of the commenting style. We conduct experiments on two benchmark datasets. As a result, our model achieved new state-of-the-art results with the micro F1-score of 75.53% and 86.60% for the two datasets with different levels for the restaurant domain. In addition, our experimental results achieved the best score for the smartphone domain with the macro F1-score of 81.10%.

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Section: Related Workmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Settingsmentioning

confidence: 99%

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Aspect-Category based Sentiment Analysis with Unified Sequence-To-Sequence Transfer Transformers

Thin¹,

Nguyen²

2023

JCSCE

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“…Mai & Le [24] proposed a sequencelabeling approach that combines BiRNN and Conditional Random Fields (CRF) to concurrently extract opinion targets and detect their associated sentiments in smartphone-related datasets. Luc Phan et al [25] give a suggested method for the Vietnamese aspect-based sentiment task is based on the Bi-LSTM architecture, using fastText word embeddings, their experiments demonstrate that this approach achieves the highest F1-scores of 84.48% for the aspect task and 63.06% for the sentiment task in Vietnamese Smartphone Feedback Dataset (UIT-ViSFD).…”

Section: Introductionmentioning

confidence: 99%

A hybrid model for aspect-based sentiment analysis on customer feedback: research on the mobile commerce sector in Vietnam

Bui

Thai

2023

Int. J. Adv. Intell. Informatics

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Feedback and comments on mobile commerce applications are extremely useful and valuable information sources that reflect the quality of products or services to determine whether data is positive or negative and help businesses monitor brand and product sentiment in customers’ feedback and understand customers’ needs. However, the increasing number of comments makes it increasingly difficult to understand customers using manual methods. To solve this problem, this study builds a hybrid research model based on aspect mining and comment classification for aspect-based sentiment analysis (ABSA) to deeply comprehend the customer and their experiences. Based on previous classification results, we first construct a dictionary of positive and negative words in the e-commerce field. Then, the POS tagging technique is applied for word classification in Vietnamese to extract aspects of model commerce related to positive or negative words. The model is implemented with machine and deep learning methods on a corpus comprising more than 1,000,000 customer opinions collected from Vietnam's four largest mobile commerce applications. Experimental results show that the Bi-LSTM method has the highest accuracy with 92.01%; it is selected for the proposed model to analyze the viewpoint of words on real data. The findings are that the proposed hybrid model can be applied to monitor online customer experience in real time, enable administrators to make timely and accurate decisions, and improve the quality of products and services to take a competitive advantage.

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Detecting Spam Reviews on Vietnamese E-Commerce Websites

Dinh

Luu

Nguyen

2022

Lecture Notes in Computer Science

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

Cited by 10 publications

References 22 publications

Aspect-Category based Sentiment Analysis with Unified Sequence-To-Sequence Transfer Transformers

Aspect-Category based Sentiment Analysis with Unified Sequence-To-Sequence Transfer Transformers

A hybrid model for aspect-based sentiment analysis on customer feedback: research on the mobile commerce sector in Vietnam

Detecting Spam Reviews on Vietnamese E-Commerce Websites

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