Using contextual features and multi-view ensemble learning in product defect identification from online discussion forums

Liu, Yao; Jiang, Cuiqing; Zhao, Huimin

doi:10.1016/j.dss.2017.10.009

Cited by 85 publications

(35 citation statements)

References 36 publications

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“…As a kind of resource which is low-cost, easily available and timely updated, online reviews have the potential to address the shortcomings of traditional survey methods (Bi et al, 2019b;Gao et al, 2018). At present, online reviews have been recognized as a data source in many fields, such as product recommendation (Siering et al, 2018), product improvement (Liu et al, 2018), product ranking (Li et al, 2010) and consumer preference analysis (Xiao et al, 2016). These studies have shown that online reviews are not only important for consumers to make purchase decisions, but also provide a low-cost and time-efficient data source for enterprises to make the product improvement (Liu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Modelling Consumer Satisfaction Based on Online Reviews Using the Improved Kano Model From the Perspective of Risk Attitude and Aspiration

Zhao

Zhang

et al. 2021

Technological and Economic Development of Economy

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With the development of e-commerce, an increasing number of online reviews can serve as a promising data source for enterprises to improve online products. This paper proposes a method for modelling consumer satisfaction based on online reviews using the improved Kano model from the perspective of risk attitude and aspiration. Firstly, the attributes concerned by consumers are extracted from online reviews, and sentiment analysis of the extracted attributes is carried out using Standford CoreNLP. Secondly, to identify the types of product attributes, an improved Kano model is proposed based on the effects of product attributes on consumer total utility. On this basis, different attribute types are illustrated from the perspective of risk attitude. Then, the consumer aspirations are mined based on the risk attitudes of different attributes and the attribute impact on consumer satisfaction. According to the risk attitudes and aspirations of different attributes, the quantified satisfaction functions are constructed to provide more objective and accurate improvement suggestions. Finally, the proposed method is applied to the hotel service improvement to illustrate the effectiveness.

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Section: Introductionmentioning

confidence: 99%

Modelling Consumer Satisfaction Based on Online Reviews Using the Improved Kano Model From the Perspective of Risk Attitude and Aspiration

Zhao

Zhang

et al. 2021

Technological and Economic Development of Economy

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“…Forums are periodic sessions where engineers and managers share experiences in their areas of expertize and responsibilities. Forums are usually formed at most meetings and conferences; however, today, "online discussion forums," are significantly expanding (Liu et al, 2018). Boundary-spanners are "human agents who translate and frame information from one community to another to promote coordination" (Hawkins and Rezazade, 2012).…”

Section: Describing the Inter-organizational Knowledge Mechanismsmentioning

confidence: 99%

Classification of inter-organizational knowledge mechanisms and their effects on networking capability: a multi-layer decision making approach

Mokhtarzadeh

Mahdiraji

Jafarpanah

et al. 2021

JKM

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Purpose The role of inter-organizational knowledge mechanisms (IOKMs) in learning networks is increasing so that the competition of business networks in providing innovations is highly dependent on the effective selection and application of these mechanisms. This study aims to argue that recognizing the classification of IOKMs and understanding their impact on networking capability (NC) makes the selection of mechanisms more effective. Design/methodology/approach With a systematic review of literature, a comprehensive list of IOKMs, their main characteristics and NCs have been extracted. The authors have used a focus group for data gathering and a hybrid multi-layer decision-making approach for data analysis. Finally, the impact of IOKMs on NC was determined. Findings By implementing a multi-layer decision-making approach, four categories of IOKMs including person-to-person, co-creation, team-oriented and informational are illustrated and their effects of NC are determined. Therefore, the findings of this research provide latecomer firms (LCFs) managers with a clear framework for selecting IOKMs. Originality/value The literature review shows that the number of knowledge mechanisms, especially their inter-organizational types, is increasing. It has made it difficult for LCFs managers to select effective and efficient mechanisms. Most of these mechanisms are listed, and few studies have classified them. Besides, research shows that fewer studies have investigated how IOKMs relate to NC. Furthermore, most studies on IOKMs have been conducted in the context of leading firms and LCFs have been neglected.

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“…Zhang and Lin (2018) proposed a multilingual approach for predicting helpfulness of reviews using statistical methods. Similarly, Liu et al (2018) proposed multi-view ensemble learning for product defect identification. They used several classifiers like Naive Bayes, Random Forest, Logistic Regression, Support Vector Machine and k-Nearest-Neighbors for their experiments.…”

Section: Best Helpful Reviews Predictionmentioning

confidence: 99%

Predicting the helpfulness score of online reviews using convolutional neural network

2019

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The smart cities aim to provide an infrastructure to their citizen that reduces both their time and effort. An example of such an available infrastructure is electronic shopping. Electronic shopping has become the hotbeds of many customers as it is easier to judge the quality of the product based on the review information. The purpose of this study is to predict the best helpful online product review, out of the several thousand reviews available for the product using review representation learning. The prediction is done using a two-layered convolutional neural network model (2-CNN). The review texts are embedded into low-dimensional vectors using a pre-trained model. To learn the best features of the review text, 3 filters are used to learn tri-gram, four-gram, and five-gram features of the text. The proposed approach is found to be better than existing machine learning based models which used handcrafted features. The very low value of mean squared error (MSE) confirms the prediction accuracy of the proposed method. The proposed method can be easily applied to any kind of review as the features are calculated only from the review text and not from other domain knowledge. The proposed model helps in predicting the helpfulness score of new reviews as soon it gets posted on the product review page. https://www.amazon.in/Mi-Redmi-5-Black-16GB/product-reviews/B077PWK5QC/ref=dpx_acr_txt?showViewpoints=1 2/4 2. Cannot use hard-disk as it does not supply enough power. But you can use a Y cable with a power bank and connect it to the phone.

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Using contextual features and multi-view ensemble learning in product defect identification from online discussion forums

Cited by 85 publications

References 36 publications

Modelling Consumer Satisfaction Based on Online Reviews Using the Improved Kano Model From the Perspective of Risk Attitude and Aspiration

Modelling Consumer Satisfaction Based on Online Reviews Using the Improved Kano Model From the Perspective of Risk Attitude and Aspiration

Classification of inter-organizational knowledge mechanisms and their effects on networking capability: a multi-layer decision making approach

Predicting the helpfulness score of online reviews using convolutional neural network

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