Will the Relaxation of COVID-19 Control Measures Have an Impact on the Chinese Internet-Using Public? Social Media-Based Topic and Sentiment Analysis

Xin, Yu; Tan, Xiaoshuang; Ren, Xiaohui

doi:10.3389/ijph.2023.1606074

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Before applying Algorithm 1, it is necessary to construct a stop word list to remove "stop words" from the segmentation results, which are meaningless words such as punctuation marks, misspelled words, and uncommon spellings [38]. This study utilizes mainstream stop word lists, including CN stop words [38], HIT stop words [40], Baidu stop words [41], MIL-SCU stop words [42], and NLTK stop words [39]. Words of other languages and symbols are removed, duplicates are eliminated, and the lists are integrated.…”

Section: Parameters Meaningsmentioning

confidence: 99%

Construction of the tourist sentiment dictionary for hotels to mining tourist demands: Based on Macao’s hotel reviews from Agoda

Wang,

Zhu,

Zhang

et al. 2024

Smart. Tour.

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<p><span lang="AZ-LATIN">Tourist hotels (or tourist accommodations) are located near tourist attractions, primarily serving tourists. In recent years, with the gradual improvement of people’s living standards around the globe, tourists’ demands and standards for tourist hotel construction have been rising accordingly. In the context of technologization and informatization, various hotel booking platforms (Agoda, Booking, Trip, etc.) cover a large amount of review data in evaluating systems to reflect tourists’ demands. Meanwhile, identifying demand-oriented reviews and extracting core consumer demands from them is crucial for optimizing hotel services and enhancing tourist satisfaction. Therefore, this study explores the demands of tourists in tourist hotels from the perspective of text sentiment analysis and takes Macao, a famous tourist destination, as an example, based on reviews of tourist hotels on the Agoda site platform. Specifics are as follows: (1) Based on pointwise mutual information (PMI) and information entropy (IE), it realizes the identification of sentiment words in the field of tourist hotels and constructs a sentiment dictionary to address the problem of poor relevance between word segmentation results; (2) It summarizes the five types of reviews containing tourist demands (positive, negative, suggestion, demand, and comparison) and their characteristics to solve the ambiguity of texts and further accurately reveal the main demands of tourists; (3) It classifies tourist demands and group similar tourist demands into the same categories to address the problem of multiple expressions for the same demand. The present study provides empirical experiences from Macao’s hotels and contributes to the literature on text sentiment analysis in tourist hotels. Furthermore, the study results could enhance the mining accuracy and provide a detailed summarization of consumer demands and directions for the sustainable optimization improvement of tourist services.</span></p>

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Section: Parameters Meaningsmentioning

confidence: 99%

Construction of the tourist sentiment dictionary for hotels to mining tourist demands: Based on Macao’s hotel reviews from Agoda

Wang,

Zhu,

Zhang

et al. 2024

Smart. Tour.

View full text Add to dashboard Cite

show abstract

“…Without the customized dictionary, "mini program" would be segmented into "mini" and "program" as two separate words. (3) Building the stopwords dictionary: The mainstream Chinese stopword dictionaries, including CN stopwords [14], HIT stopwords [15], Baidu stopwords [16], and MIL-SCU stopwords [17], were utilized to remove English words and symbols, eliminate duplicates, and integrate them. Additionally, some stopwords relevant to the context of IT services were manually added.…”

Section: Definition and Selection Of Sentiment Wordsmentioning

confidence: 99%

Using the equal sentiment enhancement with distribution (ESED) algorithm in text sentiment analysis: predicting customers purchasing intention (CPI) for IT services on freelance platforms

Zhang,

Wang,

2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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Text sentiment analysis is a mainstream method in text information analysis, which could extract important data information from text, including customer needs and opinions on the product or service. Mainstream algorithms used in text sentiment analysis typically require large amounts of data to train models, making them less applicable in research areas with limited datasets, such as information technology (IT) services. Therefore, this paper proposes a text sentiment analysis algorithm specifically designed for small sample datasets-the Equal Sentiment Enhancement with Distribution (ESED) algorithm. The kernel of this algorithm is the improvement and innovation of the gradient descent algorithm in sentiment analysis with small sample datasets. This algorithm only requires that the number of mutually independent texts exceed the number of sentiment words, and it could achieve relatively accurate calculation of word sentiment values even under the condition of small sample datasets. The present study conducts simulation experiments to illustrate the convergence and usage condition of the ESED algorithm, demonstrating its feasibility in the application of text sentiment analysis with small sample datasets. To demonstrate the superiority of the ESED algorithm in text sentiment analysis with small sample datasets, it selects the problem of customers' purchase intention (CPI) prediction for IT services on freelance platforms for comparative experiments, comparing the performance of the ESED algorithm with four mainstream text sentiment analysis algorithms. Comparative experiment results show that compared to four baseline algorithms, the ESED algorithm achieves a decrease in Mean Squared Error (MSE) of predicting the CPI by 18.0%-51.5% in the dataset with 1196 samples. Conclusively, this paper contributes to the extended application of text-based sentiment analysis research in the field of research with small sample datasets and improves the prediction of CPI for IT services on freelance platforms.

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How the crisis of trust in experts occurs on social media in China? Multiple-case analysis based on data mining

Wen,

Zhao,

Zang

et al. 2024

Humanit Soc Sci Commun

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Will the Relaxation of COVID-19 Control Measures Have an Impact on the Chinese Internet-Using Public? Social Media-Based Topic and Sentiment Analysis

Cited by 4 publications

References 32 publications

Construction of the tourist sentiment dictionary for hotels to mining tourist demands: Based on Macao’s hotel reviews from Agoda

Construction of the tourist sentiment dictionary for hotels to mining tourist demands: Based on Macao’s hotel reviews from Agoda

Using the equal sentiment enhancement with distribution (ESED) algorithm in text sentiment analysis: predicting customers purchasing intention (CPI) for IT services on freelance platforms

How the crisis of trust in experts occurs on social media in China? Multiple-case analysis based on data mining

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