Aspect-Based Sentiment Analysis Methods in Recent Years

Madhoushi, Zohreh; Hamdan, Abdul Razak; Zainudin, Suhaila

doi:10.17576/apjitm-2019-0801-07

Cited by 17 publications

(14 citation statements)

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“…The most used method for extracting the learned aspects is the frequency-based method [27,45]. Despite its simplicity, it is very effective and used by many researchers [41].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews

2020

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“…The most used method for extracting the learned aspects is the frequency-based method [27,45]. Despite its simplicity, it is very effective and used by many researchers [41].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews

2020

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“…Sentiment analysis extracts sentiments, opinions, or judgments on products or services [6]. Most sentiment analysis is carried out at the sentence level, so it does not provide sufficiently important information for decision-making.…”

Section: Introductionmentioning

confidence: 99%

“…Most sentiment analysis is carried out at the sentence level, so it does not provide sufficiently important information for decision-making. However, this information can be obtained by conducting sentiment analysis at the subsentence level or aspect level [6].…”

Section: Introductionmentioning

confidence: 99%

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Aspect-Based Sentiment Analysis on Application Review using Convolutional Neural Network

2022

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As an obligatory application during the COVID-19 pandemic by Indonesians, PeduliLindungi must have provided outstanding quality services to its users. However, as of December 2021, users’ sentiment toward the quality and service of the PeduliLindungi application was still low, with an application rating of 3.6 out of 5 on the Google Play Store. This study uses text mining techniques for the Aspect-Based Sentiment Analysis (ABSA) task in the PeduliLindungi application review, a sentiment analysis task based on the aspect category of the application. This study aims to classify the users’ sentiment on aspects of the application and provide insight and knowledge to improve the quality of the PeduliLindungi application. The ABSA method used in this study is the classification of aspects and sentiments using the Convolutional Neural Network (CNN) algorithm. The results showed that the CNN model could produce such good performance with an f1 score of 92.23% in the aspect classification and 95.13% in the sentiment classification. The results of user sentiment modelling showed the dominance of negative sentiment in the eight aspects of the application, namely Visual Experience, Scan – Check-in/Out, Vaccine Certificate, eHac, COVID Test, Register/Login, Performance and Stability, and Privacy, Data, and Security. Index Terms—Aspect-Based Sentiment Analysis, Convolution Neural Network, PeduliLindungi, Text Classification, Text Mining.

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“…The first task in Aspect-based Sentiment Analysis (ABSA) is to detect aspects of an item or product from reviews and categories each aspect into a specific group. There are different methods to detect aspects; namely, language rule methods or LM, sequential methods, topic model methods, deep learning methods, and hybrid methods, which are the combination of the above methods [1]. We have seen exciting outcomes in various NLP tasks in recent years using these emerging models [2]- [5].…”

Section: Introductionmentioning

confidence: 99%

A Similarity Score Model for Aspect Category Detection

Madhoushi¹,

Hamdan²,

Zainudin³

2021

IJACSA

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Aspect-based Sentiment Analysis (ABSA) aims to extract significant aspects of an item or product from reviews and predict the sentiment of each aspect. Previous similarity methods tend to extract aspect categories at the word level by combining Language Models (LM) in their models. A drawback for the LM model is its dependence on a large amount of labelled data for a specific domain to function well. This work proposes a mechanism to address labelled data dependency by a one-step approach experimenting to decide the best combinatory architectures of recurrent-based LM and the best semantic similarity measures for fostering a new aspect category detection model. The proposed model addresses drawbacks of previous aspect category detection models in an implicit manner. The datasets of this study, S1 and S2, are from standard SemEval online competition. The proposed model outperforms the previous baseline models in terms of the F1-score of aspect category detection. This study finds more relevant aspect categories by creating a more stable and robust model. The F1 score of our best model for aspect category detection is 79.03% in the restaurant domain for the S1 dataset. In dataset S2, the F1score is 72.65% in the laptop domain and 75.11% in the restaurant domain.

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Aspect-Based Sentiment Analysis Methods in Recent Years

Cited by 17 publications

References 0 publications

Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews

Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews

Aspect-Based Sentiment Analysis on Application Review using Convolutional Neural Network

A Similarity Score Model for Aspect Category Detection

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