An Unsupervised Hierarchical Rule Based Model for Aspect Term Extraction Augmented with Pruning Strategies

Venugopalan, Manju; Gupta, Deepa

doi:10.1016/j.procs.2020.04.303

Cited by 13 publications

(2 citation statements)

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“…Venugopalan & Gupta (2020) [16] proposed an aspect extraction solution that utilized multiple sets of rules that took advantage of the different features of its input texts. It first extracted the initial aspect terms of its input texts by considering each word's POS and Named Entity Recognition (NER) tags and their grammatical dependencies.…”

Section: -Background Studymentioning

confidence: 99%

Extracting Explicit and Implicit Aspects Using Deep Learning

Azman Busst,

Anbananthen,

Kannan

et al. 2024

Emerg Sci J

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The proliferation of user-generated content on social networks and websites has heightened the significance of sentiment analysis, also known as opinion mining, as a critical tool for comprehending people’s attitudes toward various topics. Aspect-level sentiment analysis, which considers specific aspects or features of texts, provides a more comprehensive view of sentiment analysis. The aspect-level approach encompasses both explicit and implicit aspects, where explicit aspects are readily mentioned in texts while implicit aspects are implied or inferred from contextual clues. Despite the significance of implicit aspects in the overall review, previous research has predominantly focused on explicit aspect extraction. Limited attention has been given to the extraction of implicit aspects, despite their potential impact on capturing the complete sentiment picture of texts. Therefore, this study aims to find an aspect extraction solution capable of identifying and extracting both explicit and implicit aspects from texts. This study compares various machine and deep learning models on the SemEval-2014 and SemEval-2016 restaurant datasets. The experimental analysis demonstrates that the proposed Aspect-BiLSTM model emerged as the best-performing model, achieving high accuracy in classifying both explicit and implicit aspects, with 92.9% accuracy for the 2014 and 90.7% accuracy for the 2016 datasets. Notably, the proposed solution was able to capture multiple aspects of texts, making it more robust and versatile. This study highlights the efficacy of the Aspect-BiLSTM model for aspect extraction, which will give valuable insights into the advancement of aspect-level sentiment analysis. Doi: 10.28991/ESJ-2024-08-01-05 Full Text: PDF

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Section: -Background Studymentioning

confidence: 99%

Extracting Explicit and Implicit Aspects Using Deep Learning

Azman Busst,

Anbananthen,

Kannan

et al. 2024

Emerg Sci J

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“…Dependency parser-based extraction rules have been commonly studied in recent years for opinion target extraction [22][23][24]. However, these dependency parsers are limited by language and grammatical constraints.…”

Section: Introductionmentioning

confidence: 99%

A Model for Cross-Domain Opinion Target Extraction in Sentiment Analysis

Pak¹,

Günal²

2022

Computer Systems Science and Engineering

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Opinion target extraction is one of the core tasks in sentiment analysis on text data. In recent years, dependency parser-based approaches have been commonly studied for opinion target extraction. However, dependency parsers are limited by language and grammatical constraints. Therefore, in this work, a sequential pattern-based rule mining model, which does not have such constraints, is proposed for cross-domain opinion target extraction from product reviews in unknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also reveals the difference between the concepts of opinion target and aspect, which are commonly confused in the literature. The model consists of two stages. In the first stage, the aspects of reviews are extracted from the target domain using the rules automatically generated from source domains. The aspects are also transferred from the source domains to a target domain. Moreover, aspect pruning is applied to further improve the performance of aspect extraction. In the second stage, the opinion target is extracted among the aspects extracted at the former stage using the rules automatically generated for opinion target extraction. The proposed model was evaluated on several benchmark datasets in different domains and compared against the literature. The experimental results revealed that the opinion targets of the reviews in unknown domains can be extracted with higher accuracy than those of the previous works.

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