Aspect Extraction with Bidirectional GRU and CRF

Tran, Trang Uyen; Hoang, Ha Thanh Thi; Huynh, Hiep Xuan

doi:10.1109/rivf.2019.8713663

Cited by 14 publications

(12 citation statements)

References 21 publications

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“…(1) Frequency-based methods [1,2]; (2) Rule/template-based methods [3,4]; (3) Graph theory-based methods [6][7][8]; (4) Based on CRF or combined CRF and deep learning methods [5,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Overview Of the Methods Of Opinion Targets Extractionmentioning

confidence: 99%

“…Hu [10] based on the CRF method, and with the help of the recurrent neural network (RNN) model, carried out related research on the extraction of English and Dutch named entity recognition(NER) datasets, and achieved good results in terms of accuracy. Tran [22] combined bidirectional gated recurrent unit (BiGRU) and CRF to extract aspects of the SemEval-2014 dataset, obtaining better accuracy than the state-of-the-art methods. The methods above achieved good results in terms of extraction accuracy.…”

Section: Research Situationmentioning

confidence: 99%

See 1 more Smart Citation

“Opinion Target: Opinion Word” Pairs Extraction Based on CRF

Yan

Zhou

et al. 2021

Symmetry

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With the spread of mobile applications and online interactive platforms, the number of user reviews are increasing explosively and becoming one of the most important ways for users to voice opinions. Opinion target extraction and opinion word extraction are two key procedures used to determine the helpfulness of reviews. In this paper, we implement a system to extract “opinion target:opinion word” pairs based on the Conditional Random Field (CRF). Firstly, we used the CRF model to extract opinion targets and opinion words, then combined these into pairs in order. In addition, Node.js was used to build a visualization system to display “opinion target:opinion word” pairs. In order to verify the effectiveness of the system, experiments were conducted on the Laptop and Restaurant datasets of SemEval-2014-task4, and the accuracy of the F value extracted by the model reached 86% and 90%, respectively. All the code and datasets for this experiment are available on GitHub.

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Section: Overview Of the Methods Of Opinion Targets Extractionmentioning

confidence: 99%

Section: Research Situationmentioning

confidence: 99%

“Opinion Target: Opinion Word” Pairs Extraction Based on CRF

Yan

Zhou

et al. 2021

Symmetry

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“… Poria, Cambria & Gelbukh (2016) proposed a non-linear, supervised CNN with linguistic patterns to process OE task. Tran, Hoang & Huynh (2019) proposed a model that combined BiGRU and CRF, where BiGRU considers the long-distance dependency relationship of sentences and CRF considers the relationship of label transfer. Xu et al (2018) employed two types of pre-training embedding ( i.e.…”

Section: Information Extraction (Ie)mentioning

confidence: 99%

Deep learning for aspect-based sentiment analysis: a review

Zhu¹,

Xu²,

Bao³

et al. 2022

PeerJ Computer Science

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User-generated content on various Internet platforms is growing explosively, and contains valuable information that helps decision-making. However, extracting this information accurately is still a challenge since there are massive amounts of data. Thereinto, sentiment analysis solves this problem by identifying people’s sentiments towards the opinion target. This article aims to provide an overview of deep learning for aspect-based sentiment analysis. Firstly, we give a brief introduction to the aspect-based sentiment analysis (ABSA) task. Then, we present the overall framework of the ABSA task from two different perspectives: significant subtasks and the task modeling process. Finally, challenges are proposed and summarized in the field of sentiment analysis, especially in the domain of aspect-based sentiment analysis. In addition, ABSA task also takes the relations between various objects into consideration, which is rarely discussed in the previous work.

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“…In [24] found the sentiment of each aspect in opinion text employing multiple attention control mechanisms on memory in a recurrent attention network. Our previous approaches in [25], [26] recommended the two models with the integration of CRF and RNN variants: gated recurrent unit (GRU) and IndyLSTM. We made the best use of the two-way mechanism for BiGRU and Bi-IndyLSTM to classify aspects proficiently.…”

Section: Introductionmentioning

confidence: 99%

Toward a multitask aspect-based sentiment analysis model using deep learning

Tran

Hoan

Dang

et al. 2022

IJ-AI

Self Cite

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Sentiment analysis or opinion mining is used to understand the community’s opinions on a particular product. This is a system of selection and classification of opinions on sentences or documents. At a more detailed level, aspect-based sentiment analysis makes an effort to extract and categorize sentiments on aspects of entities in opinion text. In this paper, we propose a novel supervised learning approach using deep learning techniques for a multitasking aspect-based opinion mining system that supports four main subtasks: extract opinion target, classify aspect, classify entity (category) and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of the entity. We have used a part-of-speech (POS) layer to define the words’ morphological features integrated with GloVe word embedding in the previous layer and fed to the convolutional neural network_bidirectional long-short term memory (CNN_BiLSTM) stacked construction to improve the model’s accuracy in the opinion classification process and related tasks. Our multitasking aspect-based sentiment analysis experiments on the dataset of SemEval 2016 showed that our proposed models have obtained and categorized core tasks mentioned above simultaneously and attained considerably better accurateness than the advanced researches.

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Aspect Extraction with Bidirectional GRU and CRF

Cited by 14 publications

References 21 publications

“Opinion Target: Opinion Word” Pairs Extraction Based on CRF

“Opinion Target: Opinion Word” Pairs Extraction Based on CRF

Deep learning for aspect-based sentiment analysis: a review

Toward a multitask aspect-based sentiment analysis model using deep learning

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