Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS models
Abstract To investigate the impact of real estate market sentiment on demand forecasting, this paper constructs a Weibo sentiment index incorporating emotional polarity and verifies its predictive advantage for market demand. Based on the Bidirectional Encoder Representations from Transformers - Bid...
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| Format: | Article |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-16153-8 |
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| author | Mengkai Chen Jun Wang Feilong Zhao Gaopeng Jiang |
| author_facet | Mengkai Chen Jun Wang Feilong Zhao Gaopeng Jiang |
| author_sort | Mengkai Chen |
| collection | DOAJ |
| description | Abstract To investigate the impact of real estate market sentiment on demand forecasting, this paper constructs a Weibo sentiment index incorporating emotional polarity and verifies its predictive advantage for market demand. Based on the Bidirectional Encoder Representations from Transformers - Bidirectional Long Short-Term Memory (BERT-BiLSTM) and the characteristics of China’s housing market, we classify sentiments in crawled Weibo texts and train a sentiment analysis model specifically for the Chinese real estate domain. This model accurately extracts positive, neutral, and negative sentiment features to build a high-frequency sentiment index. Simultaneously, Internet Concern index is constructed using Baidu search data as a non-directional sentiment proxy variable. Further adopting the Autoregressive Distributed Lag Mixed Data Sampling model (ADL-MIDAS), we compare the predictive performance of these two sentiment indices alongside macroeconomic variables on market demand. Experimental results show that: (1) The BERT-BiLSTM model achieves 78.5% accuracy in sentiment classification, with its F1-score outperforming traditional methods (SVM, LSTM, etc.) by over 30%; (2) The Weibo sentiment index yields a Root Mean Squared Forecast Error (RMSFE) of 1.6%-4.7% under the ADL-MIDAS framework, significantly lower than the Internet Concern index (6.7%-7.0%). The study demonstrates that integrating deep learning with high-frequency social media sentiment indiex containing emotional polarity can more effectively capture market expectation fluctuations, while simultaneously yielding superior performance for real estate market demand forecasting. |
| format | Article |
| id | doaj-art-99d659959d0a4746891a3ff554d52780 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-99d659959d0a4746891a3ff554d527802025-08-24T11:17:33ZengNature PortfolioScientific Reports2045-23222025-08-0115111610.1038/s41598-025-16153-8Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS modelsMengkai Chen0Jun Wang1Feilong Zhao2Gaopeng Jiang3School of Management Science and Engineering, Anhui University of TechnologySchool of Management Science and Engineering, Anhui University of TechnologySchool of Management Science and Engineering, Anhui University of TechnologySchool of Management Science and Engineering, Anhui University of TechnologyAbstract To investigate the impact of real estate market sentiment on demand forecasting, this paper constructs a Weibo sentiment index incorporating emotional polarity and verifies its predictive advantage for market demand. Based on the Bidirectional Encoder Representations from Transformers - Bidirectional Long Short-Term Memory (BERT-BiLSTM) and the characteristics of China’s housing market, we classify sentiments in crawled Weibo texts and train a sentiment analysis model specifically for the Chinese real estate domain. This model accurately extracts positive, neutral, and negative sentiment features to build a high-frequency sentiment index. Simultaneously, Internet Concern index is constructed using Baidu search data as a non-directional sentiment proxy variable. Further adopting the Autoregressive Distributed Lag Mixed Data Sampling model (ADL-MIDAS), we compare the predictive performance of these two sentiment indices alongside macroeconomic variables on market demand. Experimental results show that: (1) The BERT-BiLSTM model achieves 78.5% accuracy in sentiment classification, with its F1-score outperforming traditional methods (SVM, LSTM, etc.) by over 30%; (2) The Weibo sentiment index yields a Root Mean Squared Forecast Error (RMSFE) of 1.6%-4.7% under the ADL-MIDAS framework, significantly lower than the Internet Concern index (6.7%-7.0%). The study demonstrates that integrating deep learning with high-frequency social media sentiment indiex containing emotional polarity can more effectively capture market expectation fluctuations, while simultaneously yielding superior performance for real estate market demand forecasting.https://doi.org/10.1038/s41598-025-16153-8BERT-BiLSTMSentiment classificationSentiment polarityADL-MIDAS |
| spellingShingle | Mengkai Chen Jun Wang Feilong Zhao Gaopeng Jiang Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS models Scientific Reports BERT-BiLSTM Sentiment classification Sentiment polarity ADL-MIDAS |
| title | Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS models |
| title_full | Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS models |
| title_fullStr | Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS models |
| title_full_unstemmed | Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS models |
| title_short | Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS models |
| title_sort | research on sentiment index and real estate demand forecasting based on bert bilstm and adl midas models |
| topic | BERT-BiLSTM Sentiment classification Sentiment polarity ADL-MIDAS |
| url | https://doi.org/10.1038/s41598-025-16153-8 |
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