Ontology-Driven Affective Chinese Text Analysis and Evaluation Method

Xu, Linhong; Lin, Hongfei

doi:10.1007/978-3-540-74889-2_68

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…Sentiment analysis based on the Chinese sentiment dictionary developed by the Dalian University of Technology was adopted to calculate sentiment scores of the content in non‐empty reviews, with empty reviews assigned sentiment scores of 0 (Xu et al, 2008). Since the expression of gratitude in reviews may increase the overall positive sentiment, we created a new Group 4, which reflects ‘Satisfaction with gratitude (reviews with gratitude are excluded)’ by removing reviews containing expressions of gratitude from Group 3.…”

Section: Methodsmentioning

confidence: 99%

Satisfaction or gratitude? Exploring the disparate effects of physicians' knowledge sharing on patients' service evaluation in online medical consultations

Tan

Zhang

Yang

2023

Information Systems Journal

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The influence of physicians' knowledge sharing on patient satisfaction in online medical consultations (OMCs) has received extensive attention in recent years. However, patient gratitude in OMCs, another crucial outcome for physicians' knowledge sharing, has largely been overlooked. To address this gap, this study attempts to distinguish patient satisfaction from gratitude in OMCs and elucidate the relationship between the sharing process and outcomes. Drawing on the affect theory of social exchange, this study proposes a model that investigates the relative effects of physicians' informational and emotional support during the knowledge‐sharing process on patient satisfaction and gratitude, as well as the contingent roles of physicians' professional seniority and patients' disease severity. The research model is tested by using data from a well‐known online health platform in China. The results indicate that patient gratitude is associated with a more favourable service evaluation than satisfaction in OMCs. Physicians' informational support has a greater effect on patient satisfaction than emotional support, while emotional support has a greater effect on patient gratitude than informational support. Moreover, professional seniority and disease severity positively and negatively moderate the relationship between emotional support and patient gratitude, respectively. A survey‐based experiment is also adopted to validate the research model with self‐reported perceptual measures. This study contributes to the literature on patient gratitude, online healthcare service evaluation, knowledge sharing, and the affect theory of social exchange.

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Section: Methodsmentioning

confidence: 99%

Satisfaction or gratitude? Exploring the disparate effects of physicians' knowledge sharing on patients' service evaluation in online medical consultations

Tan

Zhang

Yang

2023

Information Systems Journal

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“…And one more emotion "good" is added to make it more comfortable for Chinese language analysis. The ontology is widely used in Chinese text-mining research (Xu and Lin, 2007). The seven categories of emotion words are "Happiness," "Good," "Anger," "Sadness," "Fear," "Disgust," "Surprise."…”

Section: Generating Sentiment Score Time Seriesmentioning

confidence: 99%

Exploring public mood toward commodity markets: a comparative study of user behavior on Sina Weibo and Twitter

Chen

Lai

Cai

2020

INTR

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PurposeSina Weibo and Twitter are the top microblogging platforms with billions of users. Accordingly, these two platforms could be used to understand the public mood. In this paper, the authors want to discuss how to generate and compare the public mood on Sina Weibo and Twitter. The predictive power of the public mood toward commodity markets is discussed, and the authors want to solve the problem that how to choose between Sina Weibo and Twitter when predicting crude oil prices.Design/methodology/approachAn enhanced latent Dirichlet allocation model considering term weights is implemented to generate topics from Sina Weibo and Twitter. Granger causality test and a long short-term memory neural network model are used to demonstrate that the public mood on Sina Weibo and Twitter is correlated with commodity contracts.FindingsBy comparing the topics and the public mood on Sina Weibo and Twitter, the authors find significant differences in user behavior on these two websites. Besides, the authors demonstrate that public mood on Sina Weibo and Twitter is correlated with crude oil contract prices in Shanghai International Energy Exchange and New York Mercantile Exchange, respectively.Originality/valueTwo sentiment analysis methods for Chinese (Sina Weibo) and English (Twitter) posts are introduced, which can be reused for other semantic analysis tasks. Besides, the authors present a prediction model for the practical participants in the commodity markets and introduce a method to choose between Sina Weibo and Twitter for certain prediction tasks.

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“…The English and Chinese sentiment lexicons we used are from (Wilson et al 2005) and (Xu and Lin, 2007), respectively. We further use 75 English in-tensifiers listed in (Benzinger, 1971; page 171) and 81 Chinese intensifiers from (Zhang et al, 2012).…”

Section: Lexicon-based Bilingual Sentiment Analysismentioning

confidence: 99%