Outperforming State-of-the-Art Systems for Aspect-Based Sentiment Analysis

Talafha, Bashar; Al‐Ayyoub, Mahmoud; Abuammar, Analle; Jararweh, Yaser

doi:10.1109/aiccsa47632.2019.9035290

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…The research conducted by [2] using a combination of the CNN-GRU and word2vec models resulting in an F1score of 0.67 for aspect classification and 0.66 for sentiment classification. However, [5] and [13] show a better results using a combination of contextualized word embedding for the model.…”

Section: Introductionmentioning

confidence: 97%

Aspect-Based Sentiment Analysis on Indonesian Restaurant Review Using a Combination of Convolutional Neural Network and Contextualized Word Embedding

Amalia¹,

Winarko²

2021

Indonesian J. Comput. Cybern. Syst.

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Someone's opinion on a product or service that is poured through a review is something that is quite important for the owner or potential customer. However, the large number of reviews makes it difficult for them to analyze the information contained in the reviews. Aspect-based sentiment analysis is the process of determining the sentiment polarity of a sentence based on predetermined aspects.This study aims to analyze an Indonesian restaurant review using a combination of Convolutional Neural Network and Contextualized Word Embedding models. Then it will be compared with a combination of Convolutional Neural Network and Traditional Word Embedding models. The result of aspect-classification on three models; BERT-CNN, ELMo-CNN, and Word2vec-CNN give the best results on the ELMo-CNN model with micro-average precision of 0.88, micro-average recall of 0.84, and micro-average f1-score of 0.86. Meanwhile, the sentiment-classification gives the best results on the BERT-CNN model with a precision value of 0.89, a recall of 0.89, and an f1-score of 0.91. Classification using data without stemming have almost similar results, even better than using data with stemming.

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Section: Introductionmentioning

confidence: 97%

Aspect-Based Sentiment Analysis on Indonesian Restaurant Review Using a Combination of Convolutional Neural Network and Contextualized Word Embedding

Amalia¹,

Winarko²

2021

Indonesian J. Comput. Cybern. Syst.

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“…Early work applied rule-based or statistical methods to ABSA tasks with obvious manual characteristics [8]. Lately, machine learning and neural networks have been broadly utilized in ABSA tasks [9].…”

Section: Introductionmentioning

confidence: 99%

Attentional Interactive Encoder Network Focused on Aspect for Sentiment Classification

Yang

Teng

et al. 2023

Electronics

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Aspect-based sentiment analysis (ABSA) plays a significant role in the field of big data and aims to distinguish the sentiment polarity of specific aspects in given sentences; however, the previous works on ABSA had two limitations. They mainly considered semantic features, rather than syntactic dependency features, and paid too much attention to the context words, while ignoring the high-level interaction of multiple representations of aspects themselves. To cope with these limitations, we propose a new method based on the graph convolutional network (GCN) and the multi-head attention mechanism, called the attention interactive encoder network (AIEN). The GCN was used to obtain the syntactic information that has the greatest syntactic impact on the aspect based on the syntax dependency tree. The multi-head attention mechanism can not only obtain the context-aware information of the given aspects, but also the interaction information between multiple representations of the aspect itself. The high-level information generated by the interaction of multi-dimensional features can produce a stronger representation ability for the aspect. Our experiments with the proposed model on five benchmark datasets showed that our model outperformed other works significantly. The experimental results further demonstrated the feasibility and applicability of our proposed model in the ABSA task.

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