基于BERT-BiLSTM和ADL-MIDAS模型的情绪指数和房地产需求预测研究
Mengkai Chen1, Jun Wang1, Feilong Zhao1
1School of Management Science and Engineering, Anhui University of Technology, Ma'anshan, 243032, China.
Scientific reports
|August 18, 2025
概括
这项研究介绍了使用深度学习进行房地产需求预测的微博情绪指数. 该指数结合了情绪极性,与传统方法相比,大大提高了预测准确度.
科学领域:
- 经济学 经济学 经济学
- 数据科学数据科学数据科学
- 社交媒体分析
背景情况:
- 准确的房地产需求预测对于市场稳定至关重要.
- 传统的预测模型往往难以捕捉动态的市场情绪.
- 社交媒体数据提供了一个丰富的,实时的来源来衡量公众论.
研究的目的:
- 开发和验证用于房地产市场需求预测的新兴情绪指数.
- 评估社交媒体情绪对传统指标的预测能力.
- 利用深度学习在中国房地产环境中进行细微的情绪分析.
主要方法:
- 使用双向编码器模型抓取和分类微博文本 变压器的表示 - 双向长短记忆 (BERT-BiLSTM) 模型.
- 构建一个高频微博情绪指数与情绪极性.
- 从百度搜索数据中创建一个互联网关注索引作为代理.
- 使用自回归分布式滞后混合数据采样 (ADL-MIDAS) 模型进行比较预测.
主要成果:
- 在情绪分类方面,BERT-BiLSTM模型取得了78.5%的准确性,在F1得分方面,其表现超过了传统方法的30%.
- 微博情绪指数显示,与互联网关注指数 (6.7%-7.0%) 相比,根平均平方预测误差 (RMSFE) 的1.6%-4.7%显著较低.
- 提出的情绪指数在预测房地产市场需求方面表现优越.
结论:
- 将深度学习与高频社交媒体情绪指数相结合,可以更好地捕捉市场预期波动.
- 开发的微博情绪指数为房地产市场需求预测提供了卓越的性能.
- 这种方法为了解和预测市场动态提供了更有效的工具.
相关概念视频
Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
Econometric Views (EViews)
249
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
249
Stereotype Content Model
14.9K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.9K
Scatter Plot
9.1K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
9.1K
Regression Analysis
6.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.0K
Response Surface Methodology
264
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
The process of RSM involves several key steps:
264


