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1 – 4 of 4The purpose of constructing the technology/function matrix is to analyze the patents in the target domain. The extraction of technology words is an important part of the…
Abstract
Purpose
The purpose of constructing the technology/function matrix is to analyze the patents in the target domain. The extraction of technology words is an important part of the construction of technology/function matrix. This algorithm is used to solve the problem of low efficiency of traditional Chinese process patents technology words extraction.
Design/methodology/approach
The authors propose a Chinese process patents technology words extraction method based on the improved term frequency–inverse document frequency (TF-IDF) algorithm to help technicians obtain the technology words in the target domain. According to the characteristics of Chinese process patents technology words, the TF value of candidate technology words is divided into four parts, and the corpus of IDF value calculation of candidate technology words is selected.
Findings
Through the test of Chinese process patents in the domain of path planning, this study shows that the method is feasible and practical. It can help users quickly and accurately obtain the technology words of Chinese process patents in the target domain.
Practical implications
With the increasing number of patents on the network-based patent information platform, patent analysis of massive Chinese process patents has become a research focus. The method proposed in this paper can facilitate users to extract technology words from massive Chinese process patents for patent analysis.
Originality/value
This paper aims to improve the efficiency of Chinese process patents technology words extraction. The authors hope that the proposed method can reduce the labor and time cost of Chinese process patents technology words extraction.
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Keywords
Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent…
Abstract
Purpose
Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process.
Design/methodology/approach
In this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification.
Findings
The complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model.
Originality/value
The proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.
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Doris Chenguang Wu, Chenyu Cao, Ji Wu and Mingming Hu
Wine tourism is gaining increasing popularity among Chinese tourists, making it necessary to thoroughly examine tourist behavior. While online reviews posted by wine tourists have…
Abstract
Purpose
Wine tourism is gaining increasing popularity among Chinese tourists, making it necessary to thoroughly examine tourist behavior. While online reviews posted by wine tourists have been extensively studied from the perspectives of destinations and wineries, the perspective of the tourists themselves has been overlooked. To address this gap, this study aims to identify significant attributes intrinsic to the tourism experiences of Chinese wine tourists by adopting a text-mining approach from a tourist-centric perspective.
Design/methodology/approach
The authors use topic modeling to extract these attributes, calculate topic intensity to understand tourists’ attention distribution across these attributes and conduct topical sentiment analysis to evaluate tourists’ satisfaction levels with each attribute. The authors perform importance-performance analyses (IPAs) using topic intensity and sentiment scores. Furthermore, the authors conduct semistructured in-depth interviews with Chinese wine tourists to gain insights into the underlying reasons behind the key findings.
Findings
The study identifies eleven attributes for domestic wine tourists and seven attributes for outbound wine tourists. From the reviews of both domestic and outbound tourists, three common attributes have been identified: “scenic view”, “wine tasting and purchase” and “wine knowledge”.
Practical implications
According to the results of the IPAs, there is a pressing need for enhancements in the wine tasting and purchasing experience at domestic wine attractions. Additionally, managers of domestic wine attractions should continue to prioritize the positive aspects of the family trip experience and scenic views. On the other hand, for outbound wine attractions, it is crucial for managers to maintain their efforts in providing opportunities for wine knowledge acquisition, ensuring scenic views and upholding the reputation of wine regions.
Originality/value
First, this study breaks new ground by adopting a tourist-centric perspective to extract significant attributes from real wine tourism reviews. Second, the authors conduct a comparative analysis between Chinese wine tourists who travel domestically and those who travel abroad. The third novel aspect of this study is the application of IPA based on textual review data in the context of wine tourism. Fourth, by integrating topic modeling with qualitative interviews, the authors use a mixed-method approach to gain deeper insights into the experiences of Chinese wine tourists.
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Zhaohua Deng, Rongyang Ma, Manli Wu and Richard Evans
This study analyzes the evolution of topics related to COVID-19 on Chinese social media platforms with the aim of identifying changes in netizens' concerns during the different…
Abstract
Purpose
This study analyzes the evolution of topics related to COVID-19 on Chinese social media platforms with the aim of identifying changes in netizens' concerns during the different stages of the pandemic.
Design/methodology/approach
In total, 793,947 posts were collected from Zhihu, a Chinese Question and Answer website, and Dingxiangyuan, a Chinese online healthcare community, from 31 December, 2019, to 4 August, 2021. Topics were extracted during the prodromal and outbreak stages, and in the abatement–resurgence cycle.
Findings
Netizens' concerns varied in different stages. During the prodromal and outbreak stages, netizens showed greater concern about COVID-19 news, the impact of COVID-19 and the prevention and control of COVID-19. During the first round of the abatement and resurgence stage, netizens remained concerned about COVID-19 news and the prevention and control of the pandemic, however, less attention was paid to the impact of COVID-19. During later stages, popularity grew in topics concerning the impact of COVID-19, while netizens engaged more in discussions about international events and the raising of spirits to fight the global pandemic.
Practical implications
This study contributes to the practice by providing a way for the government and policy makers to retrospect the pandemic and thereby make a good preparation to take proper measures to communicate with citizens and address their demands in similar situations in the future.
Originality/value
This study contributes to the literature by applying an adapted version of Fink's (1986) crisis life cycle to create a five-stage evolution model to understand the repeated resurgence of COVID-19 in Mainland China.
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