Aspect Based Sentiment Analysis: Restaurant Online Review Platform in Indonesia with Unsupervised Scraped Corpus in Indonesian Language

Tedjojuwono, Samuel Mahatmaputra; Neonardi, Clement

doi:10.1109/iccsai53272.2021.9609794

Cited by 2 publications

References 4 publications

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Graph-Based Semi-Supervised Deep Learning for Indonesian Aspect-Based Sentiment Analysis

Chamid

Widowati

Kusumaningrum

2022

BDCC

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Product reviews on the marketplace are interesting to research. Aspect-based sentiment analysis (ABSA) can be used to find in-depth information from a review. In one review, there can be several aspects with a polarity of sentiment. Previous research has developed ABSA, but it still has limitations in detecting aspects and sentiment classification and requires labeled data, but obtaining labeled data is very difficult. This research used a graph-based and semi-supervised approach to improve ABSA. GCN and GRN methods are used to detect aspect and opinion relationships. CNN and RNN methods are used to improve sentiment classification. A semi-supervised model was used to overcome the limitations of labeled data. The dataset used is an Indonesian-language review taken from the marketplace. A small part is labeled manually, and most are labeled automatically. The experiment results for the aspect classification by comparing the GCN and GRN methods obtained the best model using the GRN method with an F1 score = 0.97144. The experiment for sentiment classification by comparing the CNN and RNN methods obtained the best model using the CNN method with an F1 score = 0.94020. Our model can label most unlabeled data automatically and outperforms existing advanced models.

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Graph-Based Semi-Supervised Deep Learning for Indonesian Aspect-Based Sentiment Analysis

Chamid

Widowati

Kusumaningrum

2022

BDCC

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Identificação de Temas em Comentários de Restaurantes usando BERT e Modelos de Linguagem Generativa

A. de Almeida Neto,

de Melo

2024

Anais Estendidos Do XXXIX Simpósio Brasileiro De Banco De Dados (SBBD Estendido 2024)

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Este estudo investiga a aplicação de técnicas avançadas de processamento de linguagem natural (PLN) para classificar comentários sobre restaurantes de alta gastronomia no Brasil. Utilizando 4.000 sentenças de plataformas como Google Reviews, TripAdvisor e Yelp, são comparados os desempenhos de Regressão Logística Multirrótulo, BERTimbau e Sabia. O BERTimbau apresentou melhor desempenho, com macro F1-Score de 0.88 e micro F1-Score de 0.92. A análise revela variações temáticas significativas quando se observam os restaurantes individualmente, destacando a eficácia dos modelos pré-treinados em PLN e sugerindo direções para pesquisas futuras.

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Aspect Based Sentiment Analysis: Restaurant Online Review Platform in Indonesia with Unsupervised Scraped Corpus in Indonesian Language

Cited by 2 publications

References 4 publications

Graph-Based Semi-Supervised Deep Learning for Indonesian Aspect-Based Sentiment Analysis

Graph-Based Semi-Supervised Deep Learning for Indonesian Aspect-Based Sentiment Analysis

Identificação de Temas em Comentários de Restaurantes usando BERT e Modelos de Linguagem Generativa

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