Comprehensive analysis and application of geological disaster information leveraging topic modeling and sentiment mining

Abstract

Introduction:

In recent years, with the rapid advancement of urbanization in China and the successive implementation of major national strategies such as the Belt and Road Initiative, the Sichuan–Xizang Railway, and the South-to-North Water Diversion Project, the potential risks and losses from geological disasters have continued to rise. Secondary disasters—including landslides, mudslides, and barrier lakes triggered by earthquakes—have significantly intensified the overall impact, posing severe challenges to disaster monitoring, early warning, emergency response, recovery, and reconstruction efforts. In this context, how to leverage new information technologies to achieve in-depth mining and application of geological disaster data has become a critical issue in disaster risk reduction and sustainable crisis management.

Methods:

This study focuses on topic modeling and sentiment analysis of disaster-related data, using geological disasters in China as a background. First, it reviews the recent advances in topic modeling and sentiment analysis techniques. Then, based on data characteristics and applicability, two major social media platforms—Weibo (Sina Weibo) and Rednote (Xiaohongshu)—are selected as primary data sources. The advantages of the LDA topic model (e.g., its unlabeled and multi-topic capabilities) and the lightweight processing efficiency of the SnowNLP sentiment analysis algorithm are discussed. As a case study, the “1•07″earthquake in Xigaze, Tibet, in 2025 is analyzed. The LDA model is used to conduct multi-topic classification and clustering visualization of Weibo disaster topic data. Combined with the SnowNLP sentiment analysis algorithm, the phased sentiment evolution judgment application is carried out using the 6-month Rednote comment data.

Results:

The LDA model effectively extracts geological disaster-related themes—such as emergency response and post-disaster recovery—and that sentiment analysis technology can reveal phase-based patterns in public emotions.

Conclusion:

These findings provide scientific support for geological disaster emergency management and public opinion guidance. The research also expands the application potential of topic modeling and sentiment analysis in the field of geological disasters and offers a direction for future integration and optimization of multimodal social media data.

1 Introduction

China is situated at the junction of the Eurasian Plate, the Indian Ocean Plate, and the Pacific Plate, spanning the two most active seismic belts in the world: the Circum-Pacific and the Mediterranean-Himalayan belts. Its geological and tectonic environment is highly complex. In recent years, with the continuous advancement of urbanization in China, human engineering activities (such as the storage of large reservoirs and the exploitation of deep mineral resources) have been increasing day by day, as well as the intensive development of high-rise buildings and underground Spaces in cities, which have changed the propagation characteristics of seismic waves and amplified the vibration effects caused by earthquakes. As a result, under the dual influence of geological conditions and human factors, earthquake disasters in China have shown a regional tendency to occur frequently. Taking 2024 as an example, there were 1,066 earthquakes of magnitude 3.0 or above in China (Figure 1), primarily located in Xinjiang, Tibet, Sichuan, and Taiwan. Among them, the largest earthquake was the 7.3-magnitude earthquake in the sea area of Hualien County, Taiwan, on April 3rd. The frequent earthquakes mentioned above pose a continuous and significant threat to people’s lives, property, and the national territory’s ecological environment.

FIGURE 1

For geological disasters, the traditional method of obtaining disaster information primarily relies on on-site monitoring in disaster-stricken areas and real-time statistics provided by government departments. The time cycle for collecting, processing, and transmitting disaster information is long, with low efficiency, and problems such as the missed transmission of key information and time lags (Zhu et al., 2012; Du et al., 2020) occur, making it challenging to meet the high timeliness requirements of modern disaster emergency response. With the deep integration of information technology and multimedia technology, mainstream social media such as Weibo and Rednote have become essential channels for people to express emotions and obtain information in their daily lives. People are increasingly enthusiastic about expressing their views and understanding of unexpected events around them on social platforms. This not only promotes fundamental changes in the pattern of event communication (Zou and Yang, 2019; Cao and Liu, 2019) but also broadens the channels and speeds up the dissemination of various disaster information. Meanwhile, due to the unstructured nature of social media data, its high noise, heterogeneity, and semantic complexity pose significant challenges in extracting high-value and expected disaster information from it. Utilizing the correct data analysis methods to extract valuable information from multi-platform disaster data has become a key issue in the field of disaster management. The Topic Model, as a typical unsupervised text mining method, has been widely applied in the field of natural language processing, especially demonstrating excellent processing capabilities in tasks such as topic discovery and text classification (Murshed et al., 2023; Tu and Yang, 2021; Hananto et al., 2022). It can identify the focus of attention at different stages of events. Table 1 shows the research and application status of the topic model in the past 10 years. As for the field of geological disaster prevention and control, Nanehkaran et al. (2021) utilized the Fuzzy Logic-based Multi-Criteria Decision-Making (MCDM) method. Combining climate, geomorphology, tectonic earthquakes, geological hydrology, and human activity factors, the susceptibility of landslides in the Tabriz area of Iran was analyzed, and a distribution map of the high-risk regions was drawn. The area was divided into five sensitive grades, providing a scientific basis for the prevention and control of regional landslide disasters Nanehkaran et al. (2022). utilized a multi-layer perceptron (MLP) artificial neural network, combined with traditional and specific triggering factors as well as historical landslide data, to predict the rockfall susceptibility in Alborz Province, Iran. The results show that the northern part of the area is the highest-risk area. The MLP model is superior to the Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) classifiers, providing a solid foundation for regional landslide risk management. Cemiloglu et al. (2023) evaluated the landslide susceptibility in Maragheh County, Iran, based on a logistic regression model. They selected multiple topographic, climatic, geological, and human activity factors, combined with historical landslide data, to establish a model, and determined that the area as a whole is in a medium to high-risk zone. The experimental verification results show that the AUC value is 0.769, and the LR model has high reliability. At the same time, as disaster emergency management not only focuses on disaster information mining and emergency response, but also needs to take into account the long-term evolution of public opinion, public psychological recovery and post-disaster reconstruction strategies, the use of emotion analysis technology can fully understand the process of public emotional changes, determine the key points of emotional turning, and provide a scientific basis for precise rescue decisions. Therefore, an increasing number of researchers are integrating topic models and sentiment analysis techniques to apply them in the field of disaster management, conducting correlation analysis of disaster big data and public opinion control, and providing new research paradigms and technical support for emergency response, recovery, and reconstruction of various disasters (Luo et al., 2021).

The rest of this article is organized as follows: Section Two elaborates on the characteristics of social media data and the principles of LDA and SnowNLP methods; The third section presents in detail the topic classification and emotional evolution analysis experiments of the earthquake cases in Tibet. The fourth section summarizes the research results and outlines future directions for multimodal data fusion and model optimization.

2 Data and methods

2.1 Data evaluation

Weibo and Rednote, as two mainstream platforms in the Chinese social media landscape (Figures 2, 3), offer significant advantages over other platforms. Weibo pioneered the public square mode of “hot search list + topic tag”, and Rednote created the vertical community of “interest tag + personalized recommendation”, which jointly realized the accurate contact and transmission of information content between strangers. It is a representative social media platform in China. However, there are significant differences in data characteristics and research applicability. The Weibo platform utilizes highly structured topic text data as its primary information source, and its platform users generally adhere to standardized Hashtag behavior. According to different topic tag categories, an official hierarchical classification system covering 30 vertical fields is constructed. It provides the topic data with a clear direction and domain attribution characteristics. This type of structural feature offers an ideal basis for scientific research on topic classification. In contrast, the Rednote platform primarily uses short images, texts, and videos as content forms, and its topic tags are less frequently used and less standardized. Coupled with the relatively weak official classification system, the effect of traditional text topic classification methods is limited, resulting in fuzzy topic boundaries and overlapping topic fields, which makes it difficult to establish clear topic divisions and domain attributions. However, the unique review collection ecosystem of this platform shows significant research value. Its user comment data volume is enormous and interactive, and the density of emotional words is significantly higher than that of Weibo topic text, as determined by the Chi-square test (Li et al., 2022). Therefore, the emotional expression is more direct and abundant, which is perfectly suitable for sentiment analysis research. In summary, the two mainstream social platforms exhibit strong complementarity in data characteristics, providing a diversified range of research materials and methodological options for social media research. This paper takes the January 7 earthquake disaster event in Tibet as the research object and carries out macro-topic mining of the Tibet earthquake disaster on the Weibo platform. Moreover, this paper utilizes the Rednote comment data to conduct micro-emotional computing and user psychology research following the earthquake, aiming to obtain more comprehensive disaster emergency response plans and analysis of public opinion evolution.

FIGURE 2

FIGURE 3

2.2 LDA topic classification model

In 2003, Blei et al. (2003) proposed a three-level unsupervised probabilistic topic model, LDA, consisting of “document-topic-word” based on the research of PLSA (Probability Latent Semantic Analysis) model (Figure 4). LDA uses the Bag-of-Words model to represent documents, where each document is represented as a word frequency vector. At the same time, the Dirichlet prior distribution is introduced as a model parameter to enhance the model’s generalization ability. The model structure is divided into two parts: the document-topic Level and the topic-word Level. The probability distribution is used to describe the relationship between the Document topics and the composition of the Topic words. The classic LDA model has significant advantages in topic classification, including: it does not need to pre-label the document category, which is suitable for dealing with massive unlabeled text and perfectly fits the characteristics of social media data; Compared with BERTopic model (Wang et al., 2024), LDA topics are presented by word probability distribution, which is more interpretable and convenient for subsequent topic data classification. LDA allows multi-topic attribution of documents (Huang et al., 2024), which is more suitable for social attribute texts. In summary, the LDA model is selected to classify social media disaster topic data, thereby obtaining analysis results closer to the actual application scenario. Through indepth research on topic classification, the LDA model has also been extended and expanded. Figure 5 shows the key nodes in the development of the LDA model.

FIGURE 4

FIGURE 5

2.3 SnowNLP sentiment analysis algorithm

SnowNLP, as a mainstream natural language processing tool library, is primarily used to calculate text sentiment scores, distinguishing between positive and negative sentiment of words, and performing a preliminary analysis of sentiment orientation. In the sentiment analysis module, the Naive Bayes classifier (Zhang, 2020) is used to predict the sentiment polarity (positive/negative). The main steps are as follows: Firstly, the text is transformed into structured data using word segmentation and feature extraction technology. Secondly, the Naive Bayes classifier is employed to train the sentiment model, with probability smoothing and sentiment dictionary enhancement integrated to process unknown words and negative structures. Finally, the sentiment score, ranging from 0 to 1, is output, and the positive/negative tendency is classified using a simple threshold, which is suitable for short text analysis, such as social media reviews and product reviews. SnowNLP has the following advantages: with “lightweight” as its core, it strikes a balance between Chinese adaptation, development efficiency, and interpretability, providing a simple and efficient practical tool for small and medium-sized Chinese sentiment analysis in general fields. It supports custom domain dictionary injection and can add disaster terms such as “Aftershock” and “Barrier Lake”. It is suitable for vertical scenarios such as e-commerce, social media, and public opinion during disasters. It has low computational resource requirements, as well as high-cost performance in sentiment analysis experiments.

3 Case study experiment

Xigaze, Tibet, is located near the suture zone of the Yarlung Zangbo River on the southern edge of the Qinghai-Tibet Plateau. It is one of the most active seismic tectonic belts in the world. It is affected by tectonic factors such as the thrust fault (MFT) of the central front of the Himalayas and the detachment system of southern Tibet (STD). Crustal stress continuously accumulates and can only be released through the rupture of the crust’s brittle layer in the end. Therefore, on 7 January 2025, a magnitude 6.8 earthquake struck Xigaze, Tibet, resulting in 126 deaths, damage to 27,248 houses, and the collapse of 3,612 houses, as shown in Figure 6.

FIGURE 6

For this major earthquake disaster event, Weibo topic discussion and Rednote user comments were selected as research samples. Through LDA topic model construction and emotional polarity calculation, real-time monitoring of earthquake disasters and accurate research and judgment of public opinion situations were achieved, providing data support and management direction for the emergency management department. The specific experimental steps are shown in Figure 7.

FIGURE 7

3.1 Data acquisition and preprocessing

3.1.1 Media data acquisition

The data collection process in this paper is mainly divided into two steps: 1) based on the web crawler technology, the related data information is crawled from Weibo and Rednote with Tibet earthquake-related words as keywords. Because the geological disaster data information on Weibo exhibits dynamic characteristics, real-time monitoring of data is achieved by automatically crawling every hour. The keywords (“Tibet”, “Xigaze”, “Earthquake”, “Earthquake in Tibet”) for data retrieval are continually modified to ensure data integrity, thereby obtaining a more comprehensive dataset of geological disaster data. Finally, 32,114 topic data of Weibo and 13,395 comment data of Rednote are crawled; 2) Sort the data according to the release time in the Weibo data set, focus on the disaster data within 72 h after the disaster, especially strengthen the real-time retrieval and analysis of the Weibo data within 24 h after the disaster, to ensure the timeliness of the data. Figure 8 illustrates the specific collection effect of disaster social data.

FIGURE 8

3.1.2 Data language difference processing

3.2 Classification of data topics related to disasters on weibo

The Tibet earthquake disaster data crawled from the Weibo platform exhibits apparent characteristics of unlabeled, multi-topic, and structured data. LDA has the advantages of unsupervised learning ability, dimensionality reduction of high-dimensional sparse text, and the ability to mine the probability distribution of potential topics. Therefore, this paper selects the LDA model to classify the issues of the Tibet earthquake disaster data obtained from the Weibo platform.

3.2.1 Topic number selection

When using the LDA topic model to carry out topic classification research, the optimal number of topics K is often determined by the index of perplexity. In the research of text topic mining based on the LDA model, determining the optimal number of topics K is a key problem. In research, the perplexity index is usually used as the model evaluation standard (Ankner et al., 2024), and the generalization performance of the subject model is quantified by calculating the prediction effect of the model on the data. The calculation formula of the perplexity degree is shown in Formula 1:

D_test is the test set, M is the number of documents, w_d is the word sequence of document D in the test set, N_d is the total number of words in document D, and p (w_d) is the generation probability of document D by the LDA model. When the model is well fitted and has strong generalization ability, p (w_d)is close to 1, and -logp (w_d) is close to 0, and the degree of perplexity is the lowest.

During the model experiment, set the number of topics to [1:27] with an interval of 1, calculate the perplexity degree and model score corresponding to each topic, and comprehensively evaluate the optimal number of topics. As shown in Figure 9, the lowest point of the perplexity degree curve and the highest point of the model score are for the number of topics 23. At this time, the generalization effect of the model is the best, so the best performance K value is 23. Therefore, in the process of parameter selection, the perplexity of the topic is used as the main evaluation index, supplemented by the Model Score for comparison. Under the premise of ensuring the interpretability of the topic, the main parameter Settings of the experiment are determined as follows: Number of topics (num_topics): K is 23, α = 50/K, β = 0.01; The number of iterations is set to 1000 to ensure the convergence of the model under the sample size. Number of passes (training rounds): Set to 20 to balance computational efficiency and model stability; alpha and eta (hyperparameters): By adopting the “auto” mode, the model undergoes adaptive optimization during the training process, avoiding deviations caused by manual Settings. Random seed (random_state): Fixed at 42 to ensure the repeatability of the experiment.

FIGURE 9

3.2.2 Topic feature extraction and classification of disaster data

The topic model was trained using the optimal number of topics (K) and specific parameters, including the learning method, learning rate attenuation, and document-topic, before obtaining the topic-word probability distribution of the “Tibet 1·07 Earthquake” disaster, as shown in Table 4. The effect diagram of the 23 topic word clouds is shown in Figure 10.

FIGURE 10

3.2.3 Visual analysis of topic classification

To enhance the efficiency of data analysis and facilitate a deeper discussion of the results, this paper utilizes interactive visual analysis through the PyLDAvis library. PyLDAvis, as a professional topic model visualization tool, constructs a web-based dynamic visualization interface by analyzing the implicit semantic structure of the LDA model, as shown in Figure 11. The interface includes: 1) the left bubble map space mapping, the bubble represents the topic, the area reflects the distribution proportion of the topic in the data, and the bubble spacing represents the semantic similarity between topics; 2) The bar graph of word frequency on the right shows the 30 feature words with the highest relevance to the selected topic and their frequency distribution. Due to the high semantic overlap among the 23 topics obtained from the initial modeling, to avoid interpretation redundancy, this paper clusters the topics based on the distance matrix between adjacent bubbles provided by PyLDAvis. It combines keyword semantics with manual interpretation of typical documents to integrate the topics with similar content into five macro topics. To optimize the efficiency and systematicness of the subsequent overall analysis. T (1,13,16) can be combined into a new disaster topic (DT1) based on the semantic similarity of the topic (bubble spacing); T (17,19,20,21,22,23) is combined as DT2; T (4,8,10,11,12,14,15,18) is combined as DT3; T (7,9) is combined as DT4; T (2,3,5,6) is combined as DT5.

FIGURE 11

In the word cloud graph, words are displayed in different sizes and colors. The higher the frequency of words, the larger the font in the graph. According to the analysis of the cloud chart of disaster topic words (Figure 12), the topic DT5 is disaster dispatch, which focuses on the means of disposal at the initial stage of the disaster. The words “work in the earthquake area”, “press conference”, “rescue” and other words are large, indicating that after the disaster, the country should actively respond to the disaster rescue work at the first time, allocate rescue forces, quickly start the disaster rescue action to strive for the golden time of rescue, form a “full chain, three-dimensional, intelligent” emergency response mechanism, provide timely life assistance and basic life security for the affected people, and minimize casualties and property losses; DT2 refers to on-site rescue. The topic mainly includes words such as “house”, “ruins”, “fire protection” and “people”, indicating that the losses caused by disasters are mainly caused by house collapse and mass casualties, and the rescue force is primarily the fire department, which provides essential decision-making guidance and Implementation reference for subsequent rescue deployment and emergency treatment; DT3 is post disaster recovery. The topic involves various social relief forces and materials, such as “foundation”, “milk tea”, “group”, etc. through the integration and analysis of the topic data, we can fully understand the material situation in the disaster area, quickly respond to the diversified needs after the disaster, realize the precise rescue and efficient allocation of resources in the disaster area, and explore a modern disaster relief model of “technology + social collaboration”; DT4 is a disaster notification. The topic is the collection and release of basic disaster relief information such as “earthquake source”, “east longitude” and “news”, so that the public can timely and accurately obtain the on-site disaster information, understand the disaster development situation and rescue progress, control the development of public opinion, maintain social stability, and build a disaster relief environment of “government media public” mutual trust; DT1 is a post disaster response. The topic focuses on the recovery and reconstruction after the earthquake disaster. The words “country”, “emergency management department”, “People’s Daily”, “early warning” and other words account for a relatively high proportion, which fully reflects the government’s active organization and development of post-disaster reconstruction, and timely sending earthquake aftershock warning information to the masses. While restoring normal production and life in the affected areas as soon as possible, we will do our best to minimize the impact of aftershocks on secondary disasters in the disaster areas.

FIGURE 12

3.3 Overall data trend analysis of earthquake disasters

In the process of earthquake disaster emergency response, disaster monitoring and situation assessment are the core work of the emergency management department. Based on the theory of spatiotemporal data analysis, this study selects the 1·07 earthquake disaster in Tibet as a typical case, constructs a disaster information dataset with the time granularity of days, and analyzes the release time of relevant Weibo data to analyze the overall trend of disaster data, as shown in Figure 13.

FIGURE 13

Stage 1 (2025-01-07∼2025-01-08): In the early stage of the earthquake disaster, the disaster is sudden and unpredictable, causing widespread panic among the public. Affected people tend to seek help by releasing information through Weibo, resulting in a rapid growth in the amount of information on the platform. According to the life cycle theory of disaster management, this stage corresponds to a typical “emergency response period.” 1) It is necessary to start the disaster rescue mechanism quickly; 2) Effective implementation of psychological intervention strategies to stabilize the masses’ emotions; 3) Establish efficient information communication channels. The efficiency of emergency response at this stage will directly affect the overall effect of the follow-up earthquake relief work.

Stage 2 (2025-01-08∼2025-01-09): In the middle stage of earthquake disaster evolution, with the full launch of the national emergency rescue system and the systematic intervention of professional rescue forces, the disaster situation has been significantly controlled. The mode of disaster information dissemination on social media platforms has undergone structural changes, and the frequency of emergency information released by individuals through these platforms, such as Weibo, has shown a downward trend. This stage belongs to the “emergency rescue period”, and such changes are due to: 1) the government-led emergency rescue system has achieved full coverage of the affected areas; 2) Effective operation of the infrastructure repair and material allocation mechanism; 3) improvement of the disaster information disclosure system. At the same time, the security level of the affected people has been substantially improved, which marks the gradual transition of disaster response from the emergency rescue phase to the recovery and reconstruction phase.

Stage 3 (2025-01-09∼2025-01-10): In the late stage of earthquake rescue, the rescue forces of social organizations and the national professional rescue system form an institutionalized cooperation mechanism to maximize the rescue efficiency through resource integration and information sharing. With the gradual restoration of order in the disaster area and the improvement of the information disclosure mechanism, the psychological stress response of the affected people showed a trend of mitigation, and the discussion heat and information flow of topics related to the Tibet earthquake on the Weibo platform returned to the baseline level. According to the disaster management cycle theory, this stage has achieved a smooth transition from emergency rescue to recovery and reconstruction, marking the late stage of emergency response with “system recovery and capacity reconstruction” as its core.

To sum up, this experiment visually displayed the disaster situation, emergency measures, post-disaster recovery and rescue work of Tibet’s 1·07 earthquake disaster by classifying the topics of Weibo’s topic data and visualizing the word cloud results. It analyzed the different characteristics of disasters and the differences in disaster responses during the initial, middle, and later stages of the earthquake disaster. The research results provided scientific guidance for follow-up rescue forces and resource scheduling, as well as for post-disaster recovery and reconstruction work, serving as a reference for emergency rescue and disposal of similar disasters and accidents.

3.4 Sentiment analysis research

Sentiment analysis is the process of analyzing, processing, summarizing and reasoning subjective texts with emotional color (Zhao et al., 2010). Sentiment analysis based on social media review data can effectively quantify the public sentiment tendency. By constructing the emotional time evolution model, it can accurately identify the development trend of public opinion, which is very important for disaster emergency management and public opinion control. Because the comment data crawled from the Rednote platform has the characteristics of short text, colloquial, multi-form and obscure emotional expression, the hybrid sentiment analysis method of “SnowNLP + Dictionary” has the generalization ability of combining the statistical model with the domain dictionary and the high-precision recognition ability for the emotional words in a specific domain. Therefore, this paper employs a hybrid sentiment analysis method to analyze disaster event comment data obtained from the Rednote platform.

3.4.1 Hybrid sentiment analysis method

FIGURE 14

The experimental improvement evaluation standard is: positive >0.65 to reduce the misjudgment rate; negative <0.35, to improve the severity of discrimination and further enhance the robustness of emotion classification. The overall emotional distribution of the Rednote earthquake disaster comment data is shown in Figure 15, where positive emotional words accounted for 56.1%, while negative emotional words accounted for only 6.3%. This distribution characteristic indicates that, during the 1·07 earthquake disaster event in Tibet, the public as a whole exhibited an optimistic attitude, and the development of public opinion was generally favourable, with significant positive characteristics.

FIGURE 15

3.4.2 Sentiment time series analysis

FIGURE 16

Using the social public opinion data during the earthquake in Tibet, an hour week two-dimensional emotional heat map (as shown in Figure 17) was constructed to deeply explore the temporal and spatial distribution of public emotion fluctuations, in which red represents>0.75 for high positive emotions and yellow represents<0.5 for negative emotions.

FIGURE 17

3.4.2.1 Week length dimension

Mid-week peak: the fluctuation of public sentiment shows obvious cyclical characteristics. A significant emotional peak is formed on Tuesday (0.68 ± 0.07) and Wednesday (0.67 ± 0.05), which is closely related to the positive progress of relief supplies in place and the construction of temporary resettlement sites in the middle period (the third to fifth days) after the disaster. The duration of the peak is highly consistent with the “golden 72 h” window for rescue, suggesting a key sensitive period for post-disaster emotional recovery. Weekend rebound: the emotional score on Sunday (0.64 ± 0.10) increased by 7% compared with Friday (0.60 ± 0.08), revealing the “leisure effect” of public attention to disasters - during the rest days, people have more information contact time and cognitive resources to deal with disaster reconstruction information, which promotes the systematic deviation of emotional evaluation.

3.4.2.2 Hour short dimension

Emotional fluctuations showed a significant “two peaks and one valley” circadian rhythm. Morning and evening bimodal: As social users browse content late at night, they may only forward, comment, or search the next morning. The emotional information exposed at night, after sleep-dependent memory, is given a more substantial emotional weight the next day, and for other reasons. The first emotional peak (0.69 ± 0.04) appeared in the information precipitation period at 0-6, which was consistent with the delayed feedback effect of social media information dissemination at night. The second peak appeared in the social activity period (0.67 ± 0.05) from 18:00 to 23:00, which was the “golden time” of public catalytic behavior. The attention of social media users was the most concentrated, interaction was the most active, communication efficiency was the highest, and emotional interaction was the most frequent, which was the most critical time node in sentiment analysis research. The peak value (0.90) occurred at 18:00 on Tuesday, which was attributed to the fact that the disaster relief special report broadcast by CCTV’s “Topics in Focus” generated widespread positive resonance, and the emotional score increased rapidly. Midday emotional trough: A significant decline in the emotional index (0.58 ± 0.12) was observed at 12-14, which corresponded to the stage of cognitive fatigue and information overload. Public emotional expression tended to be negative, with an average score for emotion being low.

3.4.2.3 Improvement of public opinion research

Based on the above cyclical characteristics of emotional fluctuation, when analyzing the social public opinion fluctuation of similar disaster data in the future, high-quality comments on Tuesday, Wednesday and weekend evenings (18o-23 o’clock) should be taken as a priority in the data collection stage to provide more representative sample data for sentiment analysis and effectively improve the efficiency of sentiment analysis; In view of the persistent negative emotion fluctuations on Thursday and at noon, the algorithm is used to identify and timely push the disaster notification, rescue progress and other information released by the authority department, strengthen positive and positive guidance, curb the spread of negative emotions, and further control the development of public opinion.

4 Implementation of the LDA-SnowNLP composite framework

FIGURE 18

Incremental LDA: Reuse pre-trained topics, process 100,000 pieces of data within no more than 5 min, and trigger a warning when the topic proportion suddenly increases by 30%.

Optimize SnowNLP: Integrate basic sentiment score with dictionary matching score, process 100,000 entries within no more than 3 min, and trigger a warning if the sentiment score is less than 0.35 and lasts for 1 h.

Build a theme-emotion association matrix and output three-level warnings of red (≤5 s), yellow (≤10 s), and green (≤30 s). The stability of the system is guaranteed through a triple closed loop of delay, efficiency, and quality: timeout optimization scheduling, dynamic GPU expansion, and reverse tuning when the sampling verification accuracy is lower than 90%.

5 Conclusion

To quickly and sustainably obtain disaster information and its evolution trend throughout the entire process of disaster occurrence and development, and to deeply explore the social impact mechanism of the 1·07 earthquake disaster in Tibet, this research summarizes the research status of topic models over the past 10 years. It conducted retrieval analysis, topic classification, emotion analysis, and result visualization research on the 1·07 disaster in Tibet through multidisciplinary research methods, combining Weibo topic data and Rednote review data. By accurately extracting five clustering topics with “emergency, rescue, materials, source and news” as the primary keywords, this paper deeply analyzes the disaster situation, emergency measures, disaster information release, rescue direction and reconstruction strategies in the area where the earthquake occurred in Tibet, and further uses the comment data related to the disaster events on the Rednote platform to calculate the emotional score by using the mixed sentiment analysis method of “SnowNLP + Dictionary”. Through the time series analysis of 6-month emotional scores, this paper continuously reveals the characteristics of social people’s emotional response to the January 7 earthquake in Tibet and the evolution trend of public opinion. The research results not only expand the technical path of earthquake and geological disaster prevention but also innovatively apply emotional computing to the field of disaster public opinion monitoring, providing a scientific basis and a practical paradigm for improving the geological disaster emergency management system and enhancing social public opinion management capabilities. It has significant academic value and practical significance for promoting the modernization of geological disaster risk management and public security governance in the future.

References

• 1

  AmiresmailiM.ZolalaF.Nekoei-MoghadamM.SalavatianS.ChashmyazdanM.SoltaniA.et al (2021). Role of social media in earthquake: a systematic review. Iran. Red Crescent Med. J.23 (5), e447. 10.32592/ircmj.2021.23.5.447

  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  AnknerZ.BlakeneyC.SreenivasanK.MarionM.PaulM. (2024). Perplexed by perplexity: perplexity-Based data pruning with small reference models. arXiv Prepr. arxiv:2405.20541. 10.48550/arXiv.2405.20541

  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  BleiD. M.NgA. Y.JordanM. I. (2003). Latent dirichlet allocation. J. Mach. Learn. Res.3, 993–1022. 10.5555/944919.944937

  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  CaoZ. Y.LiuX. M. (2019). Socialized marketing integration of magazines in the Mobile internet environment. Publ. J.27 (6), 87. 10.13363/j.publishingjournal.2019.06.030

  • CrossRef
  • Google Scholar
  • View reference in article

• 5

  CaoZ.LiS.LiuY.JiH. (2015). A novel neural topic model and its supervised extension. Proc. AAAI Conf. Artif. Intell.29 (1), 2210–2216. 10.1609/aaai.v29i1.9499

  • CrossRef
  • Google Scholar
  • View reference in article

• 6

  CemilogluA.ZhuL.MohammednourA. B.AzarafzaM.NanehkaranY. A. (2023). Landslide susceptibility assessment for maragheh county, Iran, using the logistic regression algorithm. Land12 (7), 1397. 10.3390/land12071397

  • CrossRef
  • Google Scholar
  • View reference in article

• 7

  ChengG.YouQ.LiG.LiY.YangD.WuJ.et al (2024). Research on the application of topic models based on geological disaster information mining. Information15 (12), 795. 10.3390/info15120795

  • CrossRef
  • Google Scholar
  • View reference in article

• 8

  ContrerasD.WilkinsonS.BalanN.JamesP. (2022). Assessing post-disaster recovery using sentiment analysis: the case of l’Aquila, Italy. Earthq. Spectra38 (1), 81–108. 10.1177/87552930211036486

  • CrossRef
  • Google Scholar
  • View reference in article

• 9

  DasR.ZaheerM.DyerC. (2015). Gaussian LDA for topic models with word embeddings. Assoc. Comput. Linguistics 7th Int. Jt. Conf. Nat. Lang. Process.1, 795–804. 10.3115/v1/P15-1077

  • CrossRef
  • Google Scholar
  • View reference in article

• 10

  DuZ. Q.LiY.ZhangY. T.TanY. Q.ZhaoW. H. (2020). Knowledge graph construction method on natural disaster emergency. Geomatics Inf. Sci. Wuhan Univ.45 (9), 1344–1355. 10.13203/j.whugis20200047

  • CrossRef
  • Google Scholar
  • View reference in article

• 11

  EligüzelN.ÇetinkayaC.DereliT. (2023). Comparative analysis with topic modeling and word embedding methods after the aegean sea earthquake on Twitter. Evol. Syst.14 (2), 245–261. 10.1007/s12530-022-09450-4

  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  GhalyM. Z.LaksitoA. D. (2023). “Topic modeling of natural disasters in Indonesia using NMF,” in 2023 eighth international conference on informatics and computing, 1–6. 10.1109/ICIC60109.2023.10382064

  • CrossRef
  • Google Scholar
  • View reference in article

• 13

  HanantoV. R.SerdültU.KryssanovV. (2022). A text segmentation method for automated annotation of online customer reviews, based on topic modeling. Appl. Sci.12 (7), 3412. 10.3390/app12073412

  • CrossRef
  • Google Scholar
  • View reference in article

• 14

  HassanS. Z.AhmadK.HicksS.HalvorsenP.Al-FuqahaA.ConciN.et al (2022). Visual sentiment analysis from disaster images in social media. Sensors22 (10), 3628. 10.3390/s22103628

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  HuangZ. Y.MoG. Q.YuK. M. (2024). General text matching based on topic model. Comput. Appl. Softw.41 (5), 310–318. 10.3969/j.issn.1000-386x.2024.05.045

  • CrossRef
  • Google Scholar
  • View reference in article

• 16

  JacobiC.VanA. W.WelbersK. (2018). Quantitative analysis of large amounts of journalistic texts using topic modelling. Digit. Journal.4 (1), 89–106. 10.4324/9781315115047-7

  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  JiW.MengX.ZhangY. (2022). SPATM: a social period-aware topic model for personalized venue recommendation. IEEE Trans. Knowl. Data Eng.34 (8), 3997–4010. 10.1109/TKDE.2020.3029070

  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  KoltcovS.SurkovA.FilippovV.IgnatenkoV. (2024). Topic models with elements of neural networks: investigation of stability, coherence, and determining the optimal number of topics. PeerJ Comput. Sci.10, e1758. 10.7717/peerj-cs.1758

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 19

  KowaldD.PujariS. C.LexE. (2017). “Temporal effects on hashtag reuse in Twitter: a cognitive-inspired hashtag recommendation approach,” in Proceedings of the 26th international conference on world wide web, 1401–1410. 10.1145/3038912.3052605

  • CrossRef
  • Google Scholar
  • View reference in article

• 20

  LiC.WangH.ZhangZ.SunA.MaZ. (2016). “Topic modeling for short texts with auxiliary word embeddings,” in Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, 165–174. 10.1145/2911451.2911499

  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  LiM.BaoL.HuY.ChengS.HuX. B.GaoY. (2022). Implementation of random number online detection method based on chi square test. Microelectronics52 (3), 388–392. 10.13911/j.cnki.1004-3365.210329

  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  LiA.LiY.ShaoY.LiuB. (2023). Multiview scholar clustering with dynamic interest tracking. IEEE Trans. Knowl. Data Eng.35 (9), 9671–9684. 10.1109/TKDE.2023.3248221

  • CrossRef
  • Google Scholar
  • View reference in article

• 23

  LiuB.ZhangP.LuT.GuN. (2020). A reliable cross-site user generated content modeling method based on topic model. Knowledge-Based Syst.209, 106435. 10.1016/j.knosys.2020.106435

  • CrossRef
  • Google Scholar
  • View reference in article

• 24

  LiuC.DuJ. P.ZhouN. (2021). A cross media search method for social networks based on adversarial learning and semantic similarity. Sci. China Inf. Sci.51 (5), 779–794. 10.1360/ssi-2019-0120

  • CrossRef
  • Google Scholar
  • View reference in article

• 25

  LuoJ.WangL. H.TuS. S.SongG.HanY. (2021). Analysis of public sentiment tendency in sudden meteorological disasters based on LSTM-BLS. J. Nanjing Univ. Inf. Sci. and Technol.13 (4), 477–483. 10.13878/j.cnki.jnuist.2021.04.014

  • CrossRef
  • Google Scholar
  • View reference in article

• 26

  MengQ.XiongH. (2021). A doctor recommendation based on graph computing and LDA topic model. Int. J. Comput. Intell. Syst.14 (1), 808–817. 10.2991/ijcis.d.210205.002

  • CrossRef
  • Google Scholar
  • View reference in article

• 27

  MurshedB. A. H.MallappaS.AbawajyJ.SaifM. A. N.Al-ArikiH. D. E.AbdulwahabH. M. (2023). Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis. Artif. Intell. Rev.56, 5133–5260. 10.1007/s10462-022-10254-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28

  NanehkaranY. A.MaoY. M.AzarafzaM.KockarM. (2021). Fuzzy-based multiple decision method for landslide susceptibility and hazard assessment: a case study of tabriz, Iran. Geomechanics Eng.24 (5), 407–418. 10.12989/gae.2021.24.5.407

  • CrossRef
  • Google Scholar

• 29

  NanehkaranY. A.ZhuL. C.ChenJ. D.MohammadA.MaoY. M. (2022). Application of artificial neural networks and geographic information system to provide hazard susceptibility maps for rockfall failures. Environ. Earth Sci.81 (19), 475. 10.1007/s12665-022-10603-6

  • CrossRef
  • Google Scholar

• 30

  ParkY.LimS.GuC.SyafiandiniA. F.SongM. (2025). Forecasting topic trends of blockchain utilizing topic modeling and deep learning-based time-series prediction on different document types. J. Inf.19 (2), 101639. 10.1016/j.joi.2025.101639

  • CrossRef
  • Google Scholar

• 31

  PavithraC. B.SavithaJ. (2024). Topic modeling for evolving textual data using LDA HDP NMF BERTOPIC and DTM with a focus on research papers. J. Technol. Inf.5 (2), 53–63. 10.37802/joti.v5i2.618

  • CrossRef
  • Google Scholar

• 32

  PengX.XuQ.GanW. (2021). SBTM: a joint sentiment and behaviour topic model for online course discussion forums. J. Inf. Sci.47 (4), 517–532. 10.1177/0165551520917120

  • CrossRef
  • Google Scholar

• 33

  RuanT.KongQ.McBrideS. K.SethjiwalaA.LvQ. (2022). Cross-platform analysis of public responses to the 2019 ridgecrest earthquake sequence on Twitter and Reddit. Sci. Rep.12 (1), 1634. 10.1038/s41598-022-05359-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  ShaoX.TangG.BaoB. K. (2019). Personalized travel recommendation based on sentiment-aware multimodal topic model. IEEE Access7, 113043–113052. 10.1109/ACCESS.2019.2935155

  • CrossRef
  • Google Scholar

• 35

  ShiL.DuJ. P.LiangM. Y.KouF. F. (2019). SRTM: a sparse RNN-Topic model for discovering bursty topics in big data of social networks. J. Inf. Sci. and Eng.35 (4), 749–767. 10.6688/JISE.201907_35(4).0003

  • CrossRef
  • Google Scholar

• 36

  ShiL.SongG.ChengG.LiuX. (2020). A user-based aggregation topic model for understanding user’s preference and intention in social network. Neurocomputing413, 1–13. 10.1016/j.neucom.2020.06.099

  • CrossRef
  • Google Scholar

• 37

  ToubiaO. (2021). A poisson factorization topic model for the study of creative documents (and their summaries). J. Mark. Res.58 (6), 1142–1158. 10.1177/0022243720943209

  • CrossRef
  • Google Scholar

• 38

  TuS.YangB. (2021). Research on sentiment classification of microblog short text based on topic clustering. J. Phys. Conf. Ser.1827 (1), 012160. 10.1088/1742-6596/1827/1/012160

  • CrossRef
  • Google Scholar

• 39

  WangX. M. (2021). Feature research and visual analysis of high-dimensional landslide and debris flow disaster data. Beijing: Beijing Jiaotong University. 10.26944/d.cnki.gbfju.2021.000857

  • CrossRef
  • Google Scholar

• 40

  WangX.YangY. (2020). Neural topic model with attention for supervised learning. Int. Conf. Artif. Intell. Statistics9, 1147–1156. Available online at: https://proceedings.mlr.press/v108/wang20c.html

  • Google Scholar

• 41

  WangZ.ChenJ.ChenJ.ChenH. (2024). Identifying interdisciplinary topics and their evolution based on BERTopic. Scientometrics129 (11), 7359–7384. 10.1007/s11192-023-04776-5

  • CrossRef
  • Google Scholar

• 42

  XingZ.SuX.LiuJ.SuW.ZhangX. (2019). Spatiotemporal change analysis of earthquake emergency information based on microblog data: a case study of the 8.8 jiuzhaigou earthquake. ISPRS Int. J. Geo-Information.8 (8), 359. 10.3390/ijgi8080359

  • CrossRef
  • Google Scholar

• 43

  YangY.HuJ.LiuY.ChenX. (2020). Doctor recommendation based on an intuitionistic normal cloud model considering patient preferences. Cogn. Comput.12, 460–478. 10.1007/s12559-018-9616-3

  • CrossRef
  • Google Scholar

• 44

  YaoL.ZhangY.WeiB.ZheJ.ChenQ.et al (2017). Incorporating knowledge graph embeddings into topic modeling. Proc. AAAI Conf. Artif. Intell.31 (1), 3119–3126. 10.1609/aaai.v31i1.10951

  • CrossRef
  • Google Scholar

• 45

  YinH.ZhouX.CuiB.WangH.ZhengK.NguyenQ. V. H. (2016). Adapting to user interest drift for poi recommendation. IEEE Trans. Knowl. Data Eng.28 (10), 2566–2581. 10.1109/TKDE.2016.2580511

  • CrossRef
  • Google Scholar

• 46

  ZhangX. L. (2020). Research on naive bayes classifiers and its improved algorithms. Qingdao: Shandong University of Science and Technology. 10.27275/d.cnki.gsdku.2020.001131

  • CrossRef
  • Google Scholar

• 47

  ZhaoY. Y.QinB.LiuT. (2010). Sentiment analysis: sentiment analysis. J. Softw.21 (8), 1834–1848. 10.3724/sp.j.1001.2010.03832

  • CrossRef
  • Google Scholar

• 48

  ZhuJ. X.ZhangL. Z.ZhouX. Y.LiangG. L.WangG.CaiZ. Z.et al (2012). Application of entropy-based grey model in geological hazard assessment-a case study of qingchuan county, Sichuan Province. J. Catastrophology27 (1), 78–82. 10.3969/j.issn.1000-811X.2012.01.016

  • CrossRef
  • Google Scholar

• 49

  ZouH.YangY. F. (2019). Transmutation, shaping and cultivation of college students' values in we media context. J. South China Univ. Technol. Soc. Sci. Ed.21 (4), 118–124. 10.19366/j.cnki.1009-055X.2019.04.013

  • CrossRef
  • Google Scholar