Construction of Machine Learning Model Based on Text Mining and Ranking of Meituan Merchants

Abstract

In the Web 2.0 era, the problem of uneven quality and overload of online reviews is very serious, and the cognitive cost of obtaining valuable content from them is getting higher and higher. This paper explores an effective solution to address comment overload by means of information recommendation in order to improve the utilization of online information and information service quality. This paper proposes a review ranking recommendation scheme that focuses on the information quality of reviews and places more emphasis on satisfying users’ personal information need. The paper’s approach is used to extract and rank low-frequency keywords that appear only once in the comment set. The more useful the extracted phrases are, the more useful this review will be and the higher the usefulness votes will be, which can reflect the actual situation of this product more objectively and accurately and facilitate better consumption decisions for consumers. The experimental results show that users’ satisfaction with the perceived usefulness of the reviews is jointly influenced by the information quality of Meituan’s reviews and users’ individual information needs; the recommendation strategy achieves the organic integration of the two, and the evaluation results under three different recommendation modes show that compared with “interest recommendation” and “utility recommendation,” the satisfaction score of “fusion recommendation” is the highest

The impact of destination online review content characteristics on travel intention: experiments based on psychological distance perspectives

“…Theoretically, the notion of psychological distance is appropriate for illuminating review recipients' travel intentions related to online destination reviews. Although many studies have verified this hypothesis, some studies have found that the moderating role of psychological distance is absent in certain situations (Shin et al, 2019;Tang et al, 2021). Therefore, this study appropriately expands the dimensions of psychological distance and further examines the interaction effects of review content characteristics and psychological distances on travel intention.…”

“…Therefore, it is of great significance to identify valuable reviews and effectively use them to stimulate potential AJIM 76,1 42 tourists' travel intentions. Although a considerable amount of literature has provided insights into the impact of online reviews on tourists' decision-making in quantity (Zeng et al, 2020), timeliness (Li et al, 2019), rating (Kim et al, 2020), textual length (Zinko et al, 2020) and usefulness vote (Tang et al, 2021), it is relatively simple to examine the impact of quantitative characteristics of potential tourists' online reviews.…”

“…Consumers' evaluation of online reviews is subjectively processed and is affected by individual circumstances (Huang et al, 2022). However, some research suggests that if an online review meets the reader's needs, it will be perceived as helpful, regardless of the individual's situation (Shin et al, 2019;Tang et al, 2021). It is necessary to explore whether the impact of online reviews on potential tourists varies according to individual circumstances.…”

“…Scholars have focused on three aspects of online reviews: the reviewer, the review itself and the review receiver (Zhu et al, 2022a). Represented by the review itself, several studies have verified that travel online reviews' quantity (Zeng et al, 2020), timeliness (Li et al, 2019), rating (Kim et al, 2020), text length (Zinko et al, 2020), usefulness vote (Tang et al, 2021), consistency (Book et al, 2018), valence (Book et al, 2018), quality (Zeng et al, 2020), language style (Banerjee, 2022), details (Sparks et al, 2013), specificity (Zhang et al, 2021) and sensory cues (Kim et al, 2020;Lv et al, 2020) have significant impacts on tourist decisions. The quantitative characteristics of online travel reviews can be standardized so that it is easier to measure the variables and summarize the conclusions.…”

“…, 2020), textual length (Zinko et al. , 2020) and usefulness vote (Tang et al. , 2021), it is relatively simple to examine the impact of quantitative characteristics of potential tourists' online reviews.…”