A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

Maitama, Jaafar Zubairu; Idris, Norisma; Abdi, Asad; Shuib, Liyana; Fauzi, Rosmadi

doi:10.1109/access.2020.3031217

Cited by 38 publications

(12 citation statements)

References 120 publications

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“…This systematic study follows SLR guidelines, specifically the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model, which consists of several procedures to answer clearly defined research questions through evidence from previous studies [17], [18]. Our systematic study reporting style is based on Maitama et al's review process [19], which includes three main phases: VOLUME XX, 2017…”

Section: Review Methodologymentioning

confidence: 99%

A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

Ariyanto,

Purwitasari,

Fatichah

2024

IEEE Access

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Challenges in the big data phenomenon arise due to the existence of unstructured text data, which is very large, comes from various sources, has various formats, and contains much noise. The complexity of unstructured text data makes it difficult to extract useful information. Therefore, a process is needed to transform it into structured data to be processed further. The information Extraction (IE) process helps to extract relationships, entities, semantic roles, and events from unstructured text data by converting them into structured output. One of IE's tasks is Semantic Role Labeling (SRL), which has a crucial function in identifying semantic roles in a sentence so that it can enrich the understanding of the text. However, much of SRL development focuses on high-resource data, especially in English. The limited development of SRL in specific low-resource languages or domains is a complex challenge. This research aims to conduct a systematic study on the development of SRL for low-resource data, both in low-resource language or domainspecific contexts. The review process was carried out systematically using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model, and 54 quality papers were obtained from the filtering process (from 2018 to 2023). We review several essential points, including (1) datasets that are often used for SRL tasks and their labeling strategies for low-resource data, (2) methods that have currently been developed for SRL tasks and learning scenarios when dealing with low-resource data, (4) evaluation metrics, (5) application of SRL tasks. This review is complemented by a discussion of issues and potential solutions for developing SRL on low-resource data to help researchers develop SRL more effectively in dealing with the challenges faced with low-resource data.

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Section: Review Methodologymentioning

confidence: 99%

A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

Ariyanto,

Purwitasari,

Fatichah

2024

IEEE Access

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“…Unsupervised approaches utilize unannotated data to identify explicit or implicit aspects directly. Given that it does not require training, such a type costs less compared with other types of approaches (Maitama et al, 2020). The supervised type employs the concept of supervised learning to identify explicit and implicit aspects from online reviews, which leverage the annotated data to identify aspects of the target product.…”

Section: Review Methodologymentioning

confidence: 99%

Review of sentiment analysis: An emotional product development view

Yan

2022

Front. Eng. Manag.

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Conveying consumers’ specific emotions in new products, referred to as emotional product development or emotional design, is strategically crucial for manufacturers. Given that sentiment analysis (SA) can extract and analyze people’s opinions, sentiments, attitudes, and perceptions regarding different products/services, SA-based emotional design may provide manufacturers with real-time, direct, and rapid decision support. Despite its considerable advancements and numerous survey and review articles, SA is seldom considered in emotional design. This study is among the first efforts to conduct a thorough review of SA from the view of emotional design. The comprehensive review of aspect-level SA reveals the following: 1) All studies focus on extracting product features by mixing technical product features and consumers’ emotional perceptions. Consequently, such studies cannot capture the relationships between technical and emotional attributes and thus cannot convey specific emotions to the new products. 2) Most studies use the English language in SA, but other languages have recently received more interest in SA. Furthermore, after conceptualizing emotion as Kansei and introducing emotional product development and Kansei Engineering, a review of the data-driven emotional design is then conducted. A few efforts start to study emotional design with the help of SA. However, these studies only focus on either analyzing consumers’ preferences on product features or extracting emotional opinions from online reviews, thus cannot realize data-driven emotional product development. Finally, some research opportunities are provided. This study opens a broad door to aspect-level SA and its integration with emotional product development.

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“…In addition, "strong" can refer to aspects such as "RAM" or "processor". The work [3] points out the challenges when determining implicit aspects in a sentiment analysis problem as follows:…”

Section: Example 2 the Samsung Galaxy A8 Is Very Beautifulmentioning

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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A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

Cited by 38 publications

References 120 publications

A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

Review of sentiment analysis: An emotional product development view

Deep Learning Using Context Vectors to Identify Implicit Aspects

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