根据谷歌地图上的评论研究顾客对社区药房的看法：多变量和情感分析

IF 1.8 Q3 PHARMACOLOGY & PHARMACY

Exploratory research in clinical and social pharmacy Pub Date : 2024-09-01 DOI:10.1016/j.rcsop.2024.100498

Yahya Ali Laghbi , Mohammed Al Dhoayan

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引用次数: 0

摘要

本研究旨在利用谷歌地图平台的公开数据了解顾客对社区药房的看法。结果，从分布在沙特阿拉伯利雅得市的 512 家药店收集到 17,237 条评论。采用逻辑回归法测试多个变量与给定分数之间的关系。此外，还使用 VADER（情感推理词典）模型对书面评论进行了情感分析，随后进行了交叉分析和卡方检验。结果逻辑回归模型表明，药店得分每增加一个单位，获得更高分数的几率就会增加约 3.734 倍。Mann-Whitney U 检验表明，"书面评论 "与 "非书面评论 "之间存在显著的统计学差异（U = 39,928,072.5, p <0.001）。我们的研究发现，评论者撰写评论的意愿取决于他们的认知。本研究对使用 VADAR 进行的情感分析进行了描述性分析。卡方检验表明，评分与评论情感之间存在显著关系。

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Examining how customers perceive community pharmacies based on Google maps reviews: Multivariable and sentiment analysis

Objective

This study aims to understand customer perceptions of community pharmacies utilizing publicly available data from Google Maps platform.

Materials and methods

Python was used to scrape data with Google Maps APIs. As a result, 17,237 reviews were collected from 512 pharmacies distributed over Riyadh city, Saudi Arabia. Logistic regression was conducted to test the relationships between multiple variables and the given score. In addition, sentiment analysis using VADER (Valence Aware Dictionary for Sentiment Reasoning) model was conducted on written reviews, followed by cross-tabulation and chi-square tests.

Results

The Logistic regression model implies that a unit increase in the Pharmacy score enhances the odds of attaining a higher score by approximately 3.734 times. The Mann–Whitney U test showed that a notable and statistically significant difference between “written reviews” and “unwritten reviews” (U = 39,928,072.5, p < 0.001). The Pearson chi-square test generated a value of 2991.315 with 8 degrees of freedom, leading to a p value of 0.000.

Discussion

Our study found that the willingness of reviewers to write reviews depends on their perception. This study provides a descriptive analysis of conducted sentiment analysis using VADAR. The chi-square test indicates a significant relationship between rating scores and review sentiments.