Author:
Amalia Putri Rizki,Winarko Edi
Abstract
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.
Cited by
2 articles.
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1. Deep Learning Approach for Aspect-Based Sentiment Analysis on Indonesian Hospitals Reviews;2023 Eighth International Conference on Informatics and Computing (ICIC);2023-12-08
2. Multi-Aspect Sentiment Analysis on Tiktok Using Random Forest Classifier and Word2Vec;2022 1st International Conference on Software Engineering and Information Technology (ICoSEIT);2022-11-22