Knowledge empowered prominent aspect extraction from product reviews

Luo, Zhiyi; Huang, Shanshan; Zhu, Kenny Q.

doi:10.1016/j.ipm.2018.11.006

Cited by 42 publications

(21 citation statements)

References 10 publications

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“…Rana et al identified some domainrelated aspects on the basis of dual rules (Rana & Cheah, 2017). Luo et al used knowledge resources, such as WordNet and the new method, based on corpus statistics to extract the most appropriate aspect (Luo et al, 2019). Zhang et al (2016) extracted feature keywords by the ICTCLAS system, and proposed the associated semantic mining algorithm to mine the semantic relations among keywords.…”

Section: Unsupervised Learning Methodsmentioning

confidence: 99%

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

Zhang

Zhu

et al. 2021

Connection Science

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Attribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficult to capture more comprehensive local information from unstructured texts, which leads to the incorrect extraction of some word pairs. Aimed at the problem, this paper proposes a framework for Attribute-Level Sentiment Element Extraction (ALSEE) towards product reviews. Firstly, a small amount of sample data is selected by random sampling, and multiple features (including part of speech, word distance, dependency relationship and semantic role) which are labelled. The labelled data are used as the training set. Then, the Condition Random Field (CRF) model is applied to extract opinion targets (OT) and opinion words (OW). The self-training strategy is used to achieve the semi-supervised learning of CRF model through iterative training. Finally, target-opinion word pairs with modifying relationship are obtained by dependency parsing. Compared with the existing methods, the proposed framework can effectively extract attribute-level sentiment elements though experimental results.

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Section: Unsupervised Learning Methodsmentioning

confidence: 99%

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

Zhang

Zhu

et al. 2021

Connection Science

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“…A set of rules are applied for aspect extraction. Luo et al [59] define a new problem of extracting prominent aspects from customer review corpora. They also create a new evaluation dataset for prominent aspects extraction.…”

Section: Related Workmentioning

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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“…Image cognition is an important part of product design. Many scholars proposed different algorithms regarding cognitive matching of the nature of imagery vocabulary and established a variety of noumenon for product knowledge representation from different aspects [ 42 ], which was used to find the element matching pair between two ontology concepts [ 43 ].…”

Section: Theory Background and Related Studiesmentioning

confidence: 99%

Intelligent Design of Product Forms Based on Design Cognitive Dynamics and a Cobweb Structure

Yang

Zhang

et al. 2021

Computational Intelligence and Neuroscience

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Design is a complex, iterative, and innovative process. By traditional methods, it is difficult for designers to have an integral priori design experience to fully explore a wide range of design solutions. Therefore, refined intelligent design has become an important trend in design research. More powerful design thinking is needed in intelligent design process. Combining cognitive dynamics and a cobweb structure, an intelligent design method is proposed to formalize the innovative design process. The excavation of the dynamic mechanism of the product evolution process during product development is necessary to predict next-generation multi-image product forms from a larger design space. First, different design thinking stimulates the information source and is obtained by analyzing the designers’ thinking process when designing and mining the dynamic mechanism behind it. Based on the nonlinear cognitive cobweb process proposed by Francisco and a natural cobweb structure, the product image cognitive cobweb model (PICCM) is constructed. Then, natural cobweb predation behavior is simulated using a stimulus information source to impact the PICCM. This process uses genetic algorithms to obtain numerous offspring forms, and the PICCM’s mechanical properties are the energy loss parameters in the impact information. Furthermore, feasible solutions are selected from intelligent design sketches by the product artificial form evaluation system based on designers’ cognition, and a new product image cognitive cobweb system is reconstructed. Finally, a case study demonstrates the efficiency and feasibility of the proposed approach.

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Knowledge empowered prominent aspect extraction from product reviews

Cited by 42 publications

References 10 publications

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

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

Intelligent Design of Product Forms Based on Design Cognitive Dynamics and a Cobweb Structure

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