A sentiment analysis driven method based on public and personal preferences with correlated attributes to select online doctors

Wu, Jian; Zhang, Guangyin; Xing, Yumei; Liu, Yujia; Zhang, Zhen; Dong, Yucheng; Herrera‐Viedma, Enrique

doi:10.1007/s10489-023-04485-9

Cited by 11 publications

(3 citation statements)

References 55 publications

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“…( 17) and Eq. (18). Obviously, the larger the dPCD L j (p), L k (p) is, the greater the discrepancy and dispersion between the information contained in L j (p) and L k (p).…”

Section: Phase 3: Fuzzy Weights Determinationmentioning

confidence: 99%

“…Liu et al [17] manually extended the medical review corpus using a lexicon-based SA approach in the problem of doctor selection. By contrast, deep learning models, such as BERT [18], LSTM [6], RoBERTa [9], and Bidirectional Long and Short-Term Memory (BiL-STM) [19], show strong language understanding and transfer learning, including high accuracy in SA. BERT-BiLSTM [20] is an improved algorithm that fuses Transformer Bidirectional Encoded Representation (BERT) and BiLSTM.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Online review–driven Product Recommendation Ranking Considering Interactions Among Features and Consumers’ Psychology

Liu,

Li,

Chin

2024

Preprint

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In the digital economy, online reviews serve as valuable and real-time feedback for consumers, making them a crucial source of information for product selection. We propose a product recommendation ranking method based on sentiment analysis of online reviews to address information overload and support informed purchase decisions. The proposed model considers the interdependencies among product features and fully simulates the psychological state of consumers before and after purchasing products. First, online reviews of products are mined and preprocessed. Natural language processing techniques are employed to extract product features and perform aspect-based sentiment analysis. The results are transformed into an evaluation matrix of the probabilistic linguistic term sets (PLTSs). Then, we propose a 2-additive fuzzy measures recognition model based on multi-dimensional feature attention information to effectively identify redundant, independent, and complementary interactions among product features. Subsequently, the fuzzy weight and the proposed possibility comprehensive discrepancy measures are applied to the interactive multi-criteria decision-making based on CPT-TODIM (Cumulative Prospect Theory TODIM) method to rank the recommended priorities of alternative products. The feasibility of the proposed method is validated through a case study of smart sweeping robot products from Jingdong Mall, followed by the investigation of the impact of various psychological behaviours that influence purchase decisions on the recommendation ranking based on stochastic multi-criteria acceptability analysis (SMAA). Finally, a comparative analysis is conducted to demonstrate the advantages and effectiveness of the proposed method.

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“…( 17) and Eq. (18). Obviously, the larger the dPCD L j (p), L k (p) is, the greater the discrepancy and dispersion between the information contained in L j (p) and L k (p).…”

Section: Phase 3: Fuzzy Weights Determinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED: Online review–driven Product Recommendation Ranking Considering Interactions Among Features and Consumers’ Psychology

Liu,

Li,

Chin

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The approach surpasses traditional models, demonstrating practical and managerial benefits for online platforms contending with information overload. Wu et al [16] present a method for aiding patients in choosing the most suitable online medical consultant. The method establishes an online decision-making process, incorporating correlated attributes derived from historical data.…”

Section: Related Workmentioning

confidence: 99%

A Trust-enhanced Recommender System for Patient-Doctor Matchmaking in Online Healthcare Communities

2023

IJIES

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In this era of vast online healthcare information, patients often find it challenging to choose the most appropriate doctors from a myriad of options available on different communities. The sheer volume of choices can be overwhelming, underscoring the importance of personalized doctor recommendations. Unfortunately, many existing communities rely on generic doctor recommendations through a global ranking system, potentially neglecting the unique needs and preferences of individual patients. To address these challenges, this study introduces a novel multicriteria user-item trust-enhanced collaborative filtering (MCUITeCF) approach, specifically designed to facilitate patients in locating doctors who match their unique preferences. This proposed approach capitalizes on multi-criteria ratings and integrates user-item trust relationships, aiming to enhance the quality of recommendations, while tackling the common issues of data sparsity and the cold-start problem. An examination of the proposed approach, using a realworld healthcare multi-criteria dataset, the RateMDs dataset, reveals its effectiveness in overcoming challenges associated with data sparsity and cold-start problems. The results demonstrate improved prediction accuracy and coverage compared to benchmark approaches, namely MC user-based CF, MC item-based CF, MC semantic-based CF, MC trust-based CF, and trust-semantic enhanced MC CF. Specifically, the results indicate that the MCUITeCF approach improves the average MAE by 66% compared to all of the benchmark approaches when tested on the RateMDs dataset. When dealing with data sparsity, the MCUITeCF approach improves the average MAE by 41% and prediction coverage by 23% compared to all of the benchmark approaches. In scenarios involving cold-start items, the MCUITeCF outperformed specific benchmark methods such as MC item-based CF, MC semantic-based CF, and Trust-Semantic enhanced MC CF, registering a 21% drop in average MAE and a 28% rise in prediction coverage. Similarly, for cold-start user situations, MCUITeCF excelled by decreasing the average MAE by 29% and a substantial increase in prediction coverage by 20% compared to MC user-based CF, MC trust-based CF, and trust-semantic enhanced MC CF benchmark approaches.

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An integrated HPF-TODIM-MULTIMOORA approach for car selection through online reviews

Liu,

Xu,

2024

Ann Oper Res

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A sentiment analysis driven method based on public and personal preferences with correlated attributes to select online doctors

Cited by 11 publications

References 55 publications

RETRACTED: Online review–driven Product Recommendation Ranking Considering Interactions Among Features and Consumers’ Psychology

RETRACTED: Online review–driven Product Recommendation Ranking Considering Interactions Among Features and Consumers’ Psychology

A Trust-enhanced Recommender System for Patient-Doctor Matchmaking in Online Healthcare Communities

An integrated HPF-TODIM-MULTIMOORA approach for car selection through online reviews

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