Opinion evolution of online consumer reviews in the e-commerce environment

Wan, Yan; Ma, Baojun; Pan, Yu

doi:10.1007/s10660-017-9258-7

Cited by 39 publications

(28 citation statements)

References 28 publications

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“…They named it as "SumView". In line with that (Wan et al, 2018) discussed the way online reviews communicate with each other and how reviewers opinions evolve over time. Wu et al (2013) modeled a two-stage mechanism to investigate the importance of online reviews in consumer's willingness to pay.…”

Section: Best Helpful Reviews Predictionmentioning

confidence: 84%

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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Section: Best Helpful Reviews Predictionmentioning

confidence: 84%

Predicting the helpfulness score of online reviews using convolutional neural network

2019

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“…In the financial market, market participants, market participants, and market participation rules are closely related. In the financial simulation model, the same market participant may own multiple assets, and the same asset may be owned by multiple market participants [20]. For example, a certain exchange may trade multiple stocks at the same time, and an investor may hold multiple stocks at the same time.…”

Section: Market Asset Model Modelingmentioning

confidence: 99%

Complex Network Minority Game Model for the Financial Market Modeling and Simulation

Chen

2020

Complexity

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This paper proposes a new financial market model based on the analysis of the minority game model. The agent in this model forms a network through information sharing, and the agent uses the minority game model to realize the evolution of the system. To better describe the financial market, we also adopt a prior connection strategy for the model. The network formed by the agent has the characteristics of a scale-free network, and as the initial network connection probability increases, the growth rate of the corresponding agent’s average connection degree increases and then decreases after reaching the peak.

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“…What is more, people may not express their true opinion for several reasons, such as social desirability, cognitive bias, or political reasons (Plassmann et al, 2015). Thus, a more valid approach to measuring trust in news involves observing neural activity (Pan et al, 2018;Wan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Why Do You Trust News? The Event-Related Potential Evidence of Media Channel and News Type

et al. 2021

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Media is the principal source of public information, and people's trust in news has been a critical mechanism in social cohesion. In recent years, the vast growth of new media (e.g., internet news portals) has brought huge change to the way information is conveyed, cannibalizing much of the space of traditional media (e.g., traditional newspapers). This has led to renewed attention on media credibility. The study aims to explore the impact of media channel on trust in news and examine the role of news type. Twenty-six participants were asked to make trust–distrust decisions after reading a variety of news headlines from different media channels while undergoing electroencephalography (EEG) monitoring. The electrophysiological results showed that, for hard news (e.g., important news related to public life), the new media condition elicited smaller N100 and larger P200 amplitudes than the traditional media condition. However, for soft news (e.g., entertainment, and non-related to vital interest), there was no significant difference. The study suggests that the fitness of media channel and news type may influence the evaluation of news, impacting participants' affective arousal and attention allocation in the early stage and influencing trust in news. These results provide neurocognitive evidence of individuals' trust toward hard and soft news consumed via different media channels, yielding new insights into trust in media and contributing to media trust theory.

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Opinion evolution of online consumer reviews in the e-commerce environment

Cited by 39 publications

References 28 publications

Predicting the helpfulness score of online reviews using convolutional neural network

Predicting the helpfulness score of online reviews using convolutional neural network

Complex Network Minority Game Model for the Financial Market Modeling and Simulation

Why Do You Trust News? The Event-Related Potential Evidence of Media Channel and News Type

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