Dependency-Based Feature and Pairwise Classifier for Cross-Domain Aspect Term Extraction

Ruskanda, Fariska Zakhralativa; Purwarianti, Ayu; Widyantoro, Dwi H.

doi:10.2139/ssrn.4156881

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Syntactic features are features related to syntax, which are the rules or structures of words, phrases, or sentences. Syntactic features that are important in aspect-based sentiment analysis including Part-of-speech tagging [18], phrases structure, and dependency relationship [19]. The combination of syntactic features used in this research are part-of-speech tagging and phrases structure that were obtained through constituency parsing.…”

Section: Aspect and Opinion Extractionmentioning

confidence: 99%

SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

Sopian,

Ilyas,

Kasyidi

et al. 2024

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Twitter is a popular social media in Indonesia, and sentiment analysis on Twitter has an important role in measuring public trust, especially in taxation issues. Aspect extraction is an important task in sentiment analysis. In this research, we propose SAER, a Syntactic Aspect-opinion Extraction and Rule prediction, that used language rule-based approach using syntactic features for aspect and opinion extraction, and we compare several algorithm for rule prediction such as Random Forest Regression, Decision Tree Regression, K-Nearest Neighbor Regression (KNN), Linear Regression, Support Vector Regression (SVR), and Extreme Gradient Boosting Regression (XGBoost) that can generate rules with a tree-based approach. By employing syntactic features and rule prediction, it has been able to explore important features in a sentence. In rule prediction, comparison results show that Support Vector Regression (SVR) was identified as the most effective model for aspects rule prediction, providing the best results with a Mean Squared Error (MSE) of 0.022, Root Mean Squared Error (RMSE) of 0.150, and Mean Absolute Error (MAE) of 0.123. While XGBoost was identified as the most effective model for opinions rule prediction, with MSE of 0.013, RMSE of 0.117, and MAE of 0.075. Since we used syntactic feature-based approaches and rule prediction in this work, it is expected to be implemented for other cases, with other domain datasets.

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Section: Aspect and Opinion Extractionmentioning

confidence: 99%

SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

Sopian,

Ilyas,

Kasyidi

et al. 2024

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“…A comparative study conducted in 2018 [5] investigated language rule-based aspect extraction methods, highlighting the significance of rule completeness and accuracy in achieving successful outcomes. Moreover, the same year [6] witnessed a study on aspect extraction from financial microblogs, comparing supervised and unsupervised methods for explicit and implicit aspect identification, showing promising results.…”

Section: Related Workmentioning

confidence: 99%

Fine-Tuning BERT for Aspect Extraction in Multi-domain ABSA

Akram,

Sabir

2023

IJCAI

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Aspect extraction plays a crucial role in understanding the fine-grained nuances of text data, allowing businesses and researchers to gain deeper insights into customer opinions, sentiment distributions, and preferences. This study presents a BERT-based framework for aspect extraction in ABSA and evaluates its performance. Our research focuses on the comprehensive analysis of aspect extraction as we test our method using the SEMEVAL dataset of various consumer evaluations across diverse domains, including laptops, restaurants, and Twitter. By fine-tuning BERT on a large dataset, we aim to overcome the limitations of traditional approaches and improve the accuracy and efficiency of aspect extraction in ABSA. The experimental findings provide evidence of the efficacy of our methodology with a noteworthy aspect extraction accuracy of 0.98, highlighting its capacity to properly and consistently extract features. The article also explores the applicability of our approach to new domains and its possible applications in real-world scenarios.Povzetek: Ta študija predstavlja okvir za ugotavljanje aspekta v ABSA, ki temelji na sistemu BERT.

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Dependency-Based Feature and Pairwise Classifier for Cross-Domain Aspect Term Extraction

Cited by 2 publications

References 16 publications

SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

Fine-Tuning BERT for Aspect Extraction in Multi-domain ABSA

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