A Survey on Implicit Aspect Detection for Sentiment Analysis: Terminology, Issues, and Scope

Soni, Piyush Kumar; Rambola, Radha Krishna

doi:10.1109/access.2022.3183205

Cited by 19 publications

(3 citation statements)

References 78 publications

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“…Among them, aspect extraction is the most critical and complex issue. Studies generally classify aspects in sentimental texts into two categories, explicit and implicit aspects [12]. Explicit aspects are often represented by nouns, noun phrases, verbs, and verb phrases, such as "picture" and "run".…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Using Context Vectors to Identify Implicit Aspects

2023

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Aspects extraction is the key task in the sentiment analysis problem, which includes extraction of both explicit and implicit aspects. Identifying implicit aspects is not a new task in sentiment analysis, but it still presents many challenges. Numerous studies have addressed the issue and offered different approaches, but there are still fundamental challenges as follows: solving the context word problem; the domain-specific problem; and the implicit aspects represented by sentiment words that do not appear explicitly in the text. This paper proposes a method for identifying implicit aspects of sentiment words. To address these above challenges, the system is built on the foundation of deep learning with context vectors, dependency grammar, anaphora coreference resolution, and sentiment ontology. The dependency parser and the entity coreference resolver are adopted to filter out the sentiment-aspect pairs data generated from embedding context words. Then, a fine-tuned technique is applied, and the classifier corresponding to each sentiment word is built to identify implicit aspects. This combination has produced positive results, reaching almost 90% accuracy.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Using Context Vectors to Identify Implicit Aspects

2023

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“…Implicit aspect detection is an important problem in ABSA because it can identify aspects and opinions not explicitly mentioned in the text, thus improving the accuracy of sentiment analysis (SA). Soni and Rambola (2022) surveyed implicit aspect detection and emphasized the significance of identifying aspects not explicitly mentioned in the text to improve the accuracy of aspect extraction and polarity classification in SA. Discussions on the terminology, issues, and scope of implicit aspects were covered, and recent techniques proposed for detecting them were reviewed.…”

Section: Introductionmentioning

confidence: 99%

Exploring aspect-based sentiment quadruple extraction with implicit aspects, opinions, and ChatGPT: a comprehensive survey

Zhang,

Cheah,

Alyasiri

et al. 2024

Artif Intell Rev

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In contrast to earlier ABSA studies primarily concentrating on individual sentiment components, recent research has ventured into more complex ABSA tasks encompassing multiple elements, including pair, triplet, and quadruple sentiment analysis. Quadruple sentiment analysis, also called aspect-category-opinion-sentiment quadruple Extraction (ACOSQE), aims to dissect aspect terms, aspect categories, opinion terms, and sentiment polarities while considering implicit sentiment within sentences. Nonetheless, a comprehensive overview of ACOSQE and its corresponding solutions is currently lacking. This is the precise gap that our survey seeks to address. To be more precise, we systematically reclassify all subtasks of ABSA, reorganizing existing research from the perspective of the involved sentiment elements, with a primary focus on the latest advancements in the ACOSQE task. Regarding solutions, our survey offers a comprehensive summary of the state-of-the-art utilization of language models within the ACOSQE task. Additionally, we explore the application of ChatGPT in sentiment analysis. Finally, we review emerging trends and discuss the challenges, providing insights into potential future directions for ACOSQE within the broader context of ABSA.

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“…Implicit aspect detection is an important problem in ABSA because it can identify aspects and opinions not explicitly mentioned in the text, thus improving the accuracy of sentiment analysis. Soni and Rambola (2022) surveyed implicit aspect detection in sentiment analysis and emphasized the significance of identifying aspects not explicitly mentioned in the text to improve the accuracy of aspect extraction and polarity classification in sentiment analysis. Discussions on the terminology, issues, and scope of implicit aspects were covered, and recent techniques proposed for detecting them were reviewed.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Quadruple Extraction with Implicit Aspects and Opinions

Zhang

Cheah

Alyasiri

et al. 2023

Preprint

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Quadruple sentiment analysis is an essential task in aspect-based sentiment analysis that aims to understand people's viewpoints by analyzing four elements in text: category, aspect, opinion, and sentiment polarity. In recent years, quadruple sentiment analysis tasks have received extensive research attention for capturing comprehensive sentiment information in sentences. This paper focuses on the latest progress in quadruple sentiment analysis tasks. Additionally, the paper provides a summary of the application of methods based on pre-trained language models and datasets in quadruple sentiment analysis with implicit aspects and opinions. It discusses techniques for constructing more practical quadruple sentiment analysis systems in generative large model scenarios. The paper's final section outlines quadruple sentiment analysis research's current challenges and future directions. Moreover, the paper suggests that using advanced language models, such as ChatGPT, holds promise for improving the accuracy and efficiency of quadruple sentiment analysis. Overall, this paper sheds light on the importance of quadruple sentiment analysis and highlights opportunities for further research in this field.

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A Survey on Implicit Aspect Detection for Sentiment Analysis: Terminology, Issues, and Scope

Cited by 19 publications

References 78 publications

Deep Learning Using Context Vectors to Identify Implicit Aspects

Deep Learning Using Context Vectors to Identify Implicit Aspects

Exploring aspect-based sentiment quadruple extraction with implicit aspects, opinions, and ChatGPT: a comprehensive survey

A Survey on Aspect-Based Sentiment Quadruple Extraction with Implicit Aspects and Opinions

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