A Fuzzy Decision Support Model With Sentiment Analysis for Items Comparison in e-Commerce: The Case Study of http://PConline.com

Ji, Peng; Zhang, Hongyu; Wang, Jianqiang

doi:10.1109/tsmc.2018.2875163

Cited by 86 publications

(45 citation statements)

References 54 publications

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“…Yang et al 35 considered the influence of time series dynamics on consumer decision‐making, and proposed a product decision‐making method based on sentiment analysis and discrete dynamic IFS operator. Similar studies included those of References 36‐40 . However, the above studies simply added up the data of multiple platforms, and failed to analyze the sentiment values of different platforms independently.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similar studies included those of References. [36][37][38][39][40] However, the above studies simply added up the data of multiple platforms, and failed to analyze the sentiment values of different platforms independently. In the actual decision-making, consumers will compare the review information of the same product in different platforms, that is, comparison is made among not only "different stores" but also "different platforms."…”

Section: Fusion For Multiple Sentiment Orientations Of Online Reviewsmentioning

confidence: 99%

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A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

Yang

Ouyang

et al. 2020

Int J Intell Syst

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In the process of online shopping, consumers usually compare the review information of the same product in different e‐commerce platforms. The sentiment orientation of online reviews from different platforms interactively influences on consumers’ purchase decision. However, due to the limitation of the ability to process information manually, it is difficult for a consumer to accurately identify the sentiment orientation of all reviews one by one and describe the process of their interactive influence. To this end, we proposed an online shopping support model using deep‐learning–based opinion mining and q‐rung orthopair fuzzy interaction weighted Heronian mean (q‐ROFIWHM) operators. First, in the proposed method, the deep‐learning model is used to automatically extract different product attribute words and opinion words from online reviews, and match the corresponding attribute‐opinion pairs; meanwhile, the sentiment dictionary is used to calculate sentiment orientation, including positive, negative, and neutral sentiments. Second, the proportions of the three kinds of sentiments about each attribute of the same product are calculated. According to the proportion value of attribute sentiment from different platforms, the sentiment information is converted into multiple cross‐decision matrices, which are represented by the q‐rung orthopair fuzzy set. Third, considering the interactive characteristics of decision matrix, the q‐ROFIWHM operators are proposed to aggregate this cross‐decision information, and then the ranking result was determined by score function to support consumers' purchase decisions. Finally, an actual example of mobile phone purchase is given to verify the rationality of the proposed method, and the sensitivity and the comparison analysis are used to show its effectiveness and superiority.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Fusion For Multiple Sentiment Orientations Of Online Reviewsmentioning

confidence: 99%

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

Yang

Ouyang

et al. 2020

Int J Intell Syst

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show abstract

“…This is because many applications in the use of GT2FNs have a strong performance, compared to other fuzzy sets [21]. For example, GT2FNs are most widely applied to the fuzzy recognition, decision making, knowledge classification, medical diagnosis, clustering, control systems, databases, and so on [22][23][24][25][26][27][28][29]. With regard to patient-centered decision-making, it is not easy for patients to express the subjective ideas of the medical care.…”

Section: Introductionmentioning

confidence: 99%

An Interval Type-2 Fuzzy Similarity-Based MABAC Approach for Patient-Centered Care

Chen

Yan

2019

Mathematics

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Patient-centered care is an essential part of the implementation of integrated medicine, integrating humanistic care into nursing services, enhancing communication between caregivers and patients, and providing personalized service to patients. Based on the similarity of interval type-2 fuzzy numbers (IT2FNs), a novel similarity-based methodology is presented for the selection of the most suitable medical treatment under a patient-centered environment. First, we propose a new similarity based on the geometric properties of interval type-2 fuzzy numbers and present a new property based on the center of gravity. Meanwhile, in order to better highlight the advantages of the proposed similarity, we selected 30 samples for comparative experiments. Second, considering the straightforward logic of the multi-attributive border approximation area comparison (MABAC) method, we extended it based on similarity to make the decision more accurate. Finally, a realistic patient-centered type-2 diabetes treatment selection problem is presented to verify the practicality and effectiveness of the proposed algorithm. A comparative analysis with existing methods is also described.

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“…As a new research area in the artificial intelligence, affective computing can model the affective states of experts' decision preferences, and the convergence process of achieving consistency. The studies on affective modelling and application have attracted extensive attention (Ji, Zhang, & Wang, ; Poria, Cambria, Bajpai, & Hussain, ) since Picard put forward the affective computing theory in 1997 (Picard, ). For example, Kshirsagar () first proposed a multi‐tuple affective computing model based on “ personality–mood–emotion ” pattern.…”

Section: Introductionmentioning

confidence: 99%

A multi‐agent affective interactive MAGDM approach and its applications

Peng

Sangwoo

2019

Expert Systems

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Traditional multi‐attribute group decision‐making (MAGDM) methods focus on weights calculation of sub‐attributes and experts' preferences, but lack the discussion on the decision‐makers' affective interaction, and its influence on the decision preference and group consistency. To address this problem, the present study proposed a new multilayer affective computing model based on “personality–mood–emotion” pattern, under the multi‐agent decision system framework. In addition, we introduced the group trending index and affection‐preference incentive mechanism, which can help simulate MAGDM process and learn group experts' decision preferences. Further, we proposed a new multi‐agent affective interactive MAGDM (MAAI‐MAGDM) method, where we defined a novel group convergence index and an alternative decision entropy to explain the convergence process of decision and group consistency. Compared to the traditional MAGDM approaches, the proposed MAAI‐MAGDM method fully considered the affective features of each expert, reduced the dependence on aggregation operators and weight analysis, alleviated the workload of group experts, and effectively reduced the complexity of decision‐making calculation process. Finally, we verified that the proposed method can effectively assist the decision‐making processes by employing two numerical cases.

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A Fuzzy Decision Support Model With Sentiment Analysis for Items Comparison in e-Commerce: The Case Study of http://PConline.com

Cited by 86 publications

References 54 publications

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

An Interval Type-2 Fuzzy Similarity-Based MABAC Approach for Patient-Centered Care

A multi‐agent affective interactive MAGDM approach and its applications

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