A novel data-driven weighted sentiment analysis based on information entropy for perceived satisfaction

Product competitiveness analysis from the perspective of customer perceived helpfulness: a novel method of information fusion research

“…To a certain extent, the greater the information entropy, the greater the amount of information and the more helpful the information is. Fresneda and Gefen (2019) combined Automated Readability Index with information entropy and proposed that information entropy was a disorder measurement model, which could automatically calculate the perceived helpful information even before human reading (Wang et al , 2022b). Fresneda and Gefen (2020) indicated that information entropy significantly affected the measurement standard of perceived helpfulness in reviews by using latent semantic analysis incorporated with regression verification.…”

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

“…The lack of a sentiment dictionary suitable for the field of tourist hotels resulted in poor relevance between word segmentation results and the field of tourist hotels [26], the ambiguity of texts made it difficult to identify demands [27], and the same demand may have multiple expressions [28]. To address the problems mentioned above, this paper conducts the following research: (1) designing a sentiment word recognition algorithm based on Pointwise Mutual Information (PMI) [29] and Information Entropy (IE) [30] to identify sentiment words in the field of tourist hotels and construct a sentiment dictionary, ensuring that the word segmentation results align with this field; (2) summarizing the types of reviews containing tourist demands and their characteristics to solve the problem of ambiguity of texts; (3) accurately identifying tourist demands and group similar tourist demands into the same categories to address the problem of multiple expressions for the same demand, providing references for research on text sentiment analysis in this domain [31]. This study proposes the innovatively optimal application of the text sentiment analysis method in tourist hotel services, and the study findings could contribute to revealing the important factors affecting consumer satisfaction in tourist hotels, as well as providing directions for the smart and sustainable optimization improvement of tourist services.…”

Matchmaking in reward-based crowdfunding platforms: a hybrid machine learning approach

“…Information characteristics have two main functions: Firstly, they reduce the number of features and dimensions to enhance the generalisation ability of models and reduce overfitting. Secondly, they can enhance our understanding of existing relationships between features and eigenvalues (Wang et al, 2022). Characteristic selection can be performed using a variety of algorithms, such as low variance feature elimination, univariate feature selection, Apriori clustering algorithm, linear models and regularisation, random forest, stability selection, and recursive feature elimination (Chan et al, 2021).…”