从网络搜索查询和移动传感器数据中大规模估计和分析网络用户的情绪。

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-01-01 Epub Date: 2023-06-02 DOI:10.1089/big.2022.0211
Wataru Sasaki, Satoki Hamanaka, Satoko Miyahara, Kota Tsubouchi, Jin Nakazawa, Tadashi Okoshi
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引用次数: 0

摘要

估计网络用户当前情绪状态的能力对于在普适计算中实现以用户为中心的适时服务具有相当大的潜力。然而,很难确定用于这种估计的数据类型,也很难收集这种情绪状态的基本事实。因此,我们建立了一个模型,以易于收集和非侵入性的方式从搜索查询数据中估计情绪状态。然后,我们建立了一个从移动传感器数据中估计情绪状态的模型,作为另一个估计模型,并将其输出补充到从搜索查询中估计的模型的地面实况标签中。这种分两步建立模型的新方法有助于提高估计网络用户情绪状态的性能。我们的系统还部署在商业堆栈中,并对超过 1100 万用户进行了大规模数据分析。我们提出了一个全国性的情绪评分,它捆绑了全国用户的情绪值。它显示了人们每日和每周的情绪节奏,并解释了 COVID-19 大流行期间的情绪起伏,这与 COVID-19 新病例的数量成反比。它能检测到同时影响许多用户情绪状态的大新闻,即使是在时间分辨率很细的情况下,如数小时。此外,我们还发现了某类广告,点击此类广告的用户的情绪有明显的变化趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large-Scale Estimation and Analysis of Web Users' Mood from Web Search Query and Mobile Sensor Data.

The ability to estimate the current mood states of web users has considerable potential for realizing user-centric opportune services in pervasive computing. However, it is difficult to determine the data type used for such estimation and collect the ground truth of such mood states. Therefore, we built a model to estimate the mood states from search-query data in an easy-to-collect and non-invasive manner. Then, we built a model to estimate mood states from mobile sensor data as another estimation model and supplemented its output to the ground-truth label of the model estimated from search queries. This novel two-step model building contributed to boosting the performance of estimating the mood states of web users. Our system was also deployed in the commercial stack, and large-scale data analysis with >11 million users was conducted. We proposed a nationwide mood score, which bundles the mood values of users across the country. It shows the daily and weekly rhythm of people's moods and explains the ups and downs of moods during the COVID-19 pandemic, which is inversely synchronized to the number of new COVID-19 cases. It detects big news that simultaneously affects the mood states of many users, even under fine-grained time resolution, such as the order of hours. In addition, we identified a certain class of advertisements that indicated a clear tendency in the mood of the users who clicked such advertisements.

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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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