Sensory Evaluation of Low-Carbon City Tourism by Gray Relational Analysis

Cheng, Yueh-Te; Liang, Ru-Qian; Ho, Meei-Chen; Wu, Chih‐Fu

doi:10.1051/e3sconf/202122801003

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Recognizing that utilizing a standardized method for calculating the gray correlation coefficients of each assessment index can significantly enhance the model's accuracy and convergence speed [43,44], this study integrates the gray correlation coefficients into the VIKOR model to enhance the validity of decision results. The calculation steps are outlined below:…”

Section: Process 3: Assessment Of Ugc Short Video Valuementioning

confidence: 99%

Value Assessment of UGC Short Videos through Element Mining and Data Analysis

Fang,

Ni,

Zhang

2023

Applied Sciences

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UGC short videos play a crucial role in sharing information and disseminating content in the era of new information technology. Accurately assessing the value of UGC short videos is highly significant for the sustainable development of self-media platforms and the secure governance of cyberspace. This study proposes a method for assessing the value of UGC short videos from the perspective of element mining and data analysis. The method involves three steps. Firstly, the text clustering algorithm and topic mapping visualization technology are utilized to identify elements for assessing the value of UGC short videos and construct an assessment index system. Secondly, structured data indexes are quantified using platform data statistics, while unstructured data indexes are quantified using the LSTM fine-grained sentiment analysis model. Lastly, the VIKOR model, incorporating an improved gray correlation coefficient, is employed to effectively evaluate the value of UGC short videos. The empirical results indicate that the value of current domestic UGC short videos is primarily associated with three dimensions: the creators, the platforms, and the users. It encompasses 11 value elements, including fan popularity, economic returns of creation, and frequency of interaction. Additionally, we assess the value of short videos within the mainstream partitions of the Bilibili platform and generate a value radar chart. Our findings reveal that short videos in game partitions generate higher revenue for creators and platforms but may neglect users’ needs for knowledge, culture, and other content. Conversely, short videos in the knowledge, food, and music partitions demonstrate specific distinctions in fulfilling users’ requirements. Ultimately, we offer personalized recommendations for the future development of high-value UGC short videos within the mainstream partitions.

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Section: Process 3: Assessment Of Ugc Short Video Valuementioning

confidence: 99%

Value Assessment of UGC Short Videos through Element Mining and Data Analysis

Fang,

Ni,

Zhang

2023

Applied Sciences

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“…For future research, the authors' intent is to apply other quantitative methods, such as grey relational [56][57][58], or neural network [53] analysis, for example, and to include other important available dimensions for the tourism industry, such as a tourism competitiveness index or other tourism indices, as well as details regarding the impact of the COVID-19 pandemic on the most-visited Romanian counties and regions, such as Bras , ov, Prahova, Sibiu, Maramures , , Bucovina, the Danube Delta, and the Black Sea beach.…”

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confidence: 99%

Google Mobility Data as a Predictor for Tourism in Romania during the COVID-19 Pandemic—A Structural Equation Modeling Approach for Big Data

2022

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Our exploratory research focuses on the possible relations between tourism and the mobility of people, using short longitudinal data for mobility dimensions during the COVID-19 pandemic. One of these is real-time, exhaustive type data, published by Google, about the mobility of people in six different dimensions, (retail, parks, residential, workplace, grocery, and transit). The aim is to analyze the directional, intensity, causal, and complex interplay between the statistical data of tourism and mobility data for Romanian counties. The main objective is to determine if real-world big data can be linked with tourism arrivals in the first 14 months of the pandemic. We have found, using correlations, factorial analysis (PCA), regression models, and SEM, that there are strong and/or medium relationships between retail and parks and overnights, and weak or no relations between other mobility dimensions (workplace, transit). By applying factorial analysis (PCA), we have regrouped the six Google Mobility dimensions into two new factors that are good predictors for Romanian tourism at the county location. These findings can help provide a better understanding of the relationship between the real movement of people in different urban areas and the tourism phenomenon: the GM parks dimension best predicts tourism indicators (overnights), the GM residential dimension correlates inversely with the tourism indicator, and the rest of the GM indices are generally weak predictors for tourism. A more complex analysis could signal the potential and the character of tourism in different destinations, by territorially and chronologically determining the GM indices that are better linked with the tourism statistical indicators. Further research is required to establish forecasting models using Google Mobility data.

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Sensory Evaluation of Low-Carbon City Tourism by Gray Relational Analysis

Cited by 2 publications

References 15 publications

Value Assessment of UGC Short Videos through Element Mining and Data Analysis

Value Assessment of UGC Short Videos through Element Mining and Data Analysis

Google Mobility Data as a Predictor for Tourism in Romania during the COVID-19 Pandemic—A Structural Equation Modeling Approach for Big Data

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