Analysis of User Comments Based on Topic Modeling using LDA on OVO E-Wallet

This study aimed to assess the different needs of patients with breast cancer and their families in online health communities at different treatment phases using a Latent Dirichlet Allocation (LDA) model. Using Python, breast cancer-related posts were collected from two online health communities: patient-to-patient and patient-to-doctor. After data cleaning, eligible posts were categorized based on the treatment phase. Subsequently, an LDA model identifying the distinct need-related topics for each phase of treatment, including data preprocessing and LDA topic modeling, was established. Additionally, the demographic and interactive features of the posts were manually analyzed. We collected 84,043 posts, of which 9504 posts were included after data cleaning. Early diagnosis and rehabilitation treatment phases had the highest and lowest number of posts, respectively. LDA identified 11 topics: three in the initial diagnosis phase and two in each of the remaining treatment phases. The topics included disease outcomes, diagnosis analysis, treatment information, and emotional support in the initial diagnosis phase; surgical options and outcomes, postoperative care, and treatment planning in the perioperative treatment phase; treatment options and costs, side effects management, and disease prognosis assessment in the non-operative treatment phase; diagnosis and treatment options, disease prognosis, and emotional support in the relapse and metastasis treatment phase; and follow-up and recurrence concerns, physical symptoms, and lifestyle adjustments in the rehabilitation treatment phase. The needs of patients with breast cancer and their families differ across various phases of cancer therapy. Therefore, specific information or emotional assistance should be tailored to each phase of treatment based on the unique needs of patients and their families.

The study aims to investigate the integration of artificial intelligence technology into Weibo sentiment analysis, aiming to enhance the effectiveness of Weibo in human-computer interaction education. Initially, the Weibo sentiment dictionary is created, and a conventional model for sentiment analysis of user-forwarded Weibo is introduced, specifically the Latent Dirichlet Allocation (LDA) model. Then, the deep learning models in the field of artificial intelligence, namely, the convolutional neural network (CNN) model and the long short-term memory network (LSTM) model, are proposed. Tencent Weibo data set is obtained through Application Programming Interface (API) crawler. The experimental environment of the deep learning model is analyzed and the data set is preprocessed. The results show that when the number of topics is 120, the relative maximum value of F1 is 69.92% and 69.96% with and without the introduction of emotional features in the LDA model, respectively. The accuracy of CNN model and LSTM model is 0.793 and 0.849, respectively. In the three cases of user characteristics, user characteristics + Weibo features, and use characteristics + Weibo features + relationship characteristics, the polarity of the forwarded comments of the LDA model doesn’t change much. In conclusion, the LDA model demonstrates universality and accuracy in sentiment analysis of user-forwarded Weibo, while LSTM proves to be more suitable for sentiment classification in this context. Leveraging the LDA deep learning model, LSTM effectively analyzes the sentiment of users forwarding Weibo. These findings serve as an experimental foundation for the efficient integration of Weibo in human-computer interaction education.

Remote sensing images are primary data sources for land use classification. High spatial resolution images enable more accurate analysis and identification of land cover types. However, a higher spatial resolution also brings new challenges to the existing classification methods. In the low-level feature spaces of remote sensing images, it is difficult to improve classification performance by modifying classifiers. Probabilistic topic models can connect low-level features and high-level semantics of remote sensing images. Latent Dirichlet allocation (LDA) models are representatives of probabilistic topic models. However, at present, probabilistic topic models are mainly adopted for scene classification and image retrieval in remote sensing image analysis only. In this study, multiscale segmentation was employed to construct bag-of-words (BoW) representations of high-resolution images. The segmented patches were then utilized as “image documents.” A structural topic model was used with an LDA model to import spatial information from the image documents at two levels: topical prevalence and topical content in the form of covariates. In this way, latent topic features in image documents can be more accurately deduced. The proposed method showed more satisfactory classification performance than standard LDA models and demonstrated a certain degree of robustness against the changes in the segmentation scale. Acknowledgement for the data support from “Yangtze River Delta Science Data Center, National Earth System Science Data Center, National Science & Technology Infrastructure of China ( http://nnu.geodata.cn:8008 )”.

We present a new way called Persona Analysis with Text Topic Modelling (PATTM), which tries to learn the role of personae according to the literal descriptions. It is similar to Latent Dirichlet Allocation (LDA) and Author Topic (AT) model, with the attribute of allowing all text to join in the topic modelling process, even when there is no persona in the text. We experiment on the “Libya Event” data set which contains more than 4,000 texts collected from the Internet. The PATTM gives lower perplexity than LDA and AT model on the data set.

A questionnaire text mining model is built based on latent semantic analysis (LSA). Take open questionnaire as the research object and improve Latent Dirichlet Allocation (LDA) model according to the text characteristics, a BM25-LDA model is built. By comparing the LDA model and BM25-LDA model on the open questionnaire text, we can verify that the performance of BM25-LDA mode is better than that of original LDA model.

Latent Dirichlet allocation (LDA) is a statistical model that is often used to discover topics or themes in a large collection of documents. In the LDA model, topics are modeled as discrete distributions over a finite vocabulary of words. The LDA is also a popular choice to model other datasets spanning a discrete domain, such as population genetics and social networks. However, in order to model data spanning a continuous domain with the LDA, discrete approximations of the data need to be made. These discrete approximations to continuous data can lead to loss of information and may not represent the true structure of the underlying data. We present an augmented version of the LDA topic model, where topics are represented using Gaussian mixture models (GMMs), which are multi-modal distributions spanning a continuous domain. This augmentation of the LDA topic model with Gaussian mixture topics is denoted by the GMM-LDA model. We use Gibbs sampling to infer model parameters. We demonstrate the utility of the GMM-LDA model by applying it to the problem of clustering sleep states in electroencephalography (EEG) data. Results are presented demonstrating superior clustering performance with our GMM-LDA algorithm compared to the standard LDA and other clustering algorithms.

A text mining approach based on latent Dirichlet allocation (LDA) is proposed to analyze the competency characteristics. First, we briefly introduce the principle and hypothesis of latent Dirichlet allocation (LDA) model. Second, we elaborate the idea of using LDA topic model to extract competency, Then, we use Chinese text materials such as biographies and interviews of Chinese scientific and technological talents as data sources to conduct experiments, and obtain four competency topics including knowledge, attitude, quality and values. The research results preliminarily verify the effectiveness of latent Dirichlet allocation (LDA) model in competency research, but there are still many details to be improved in the future.

Topic models have recently shown to be really useful tools for the analysis of microarray experiments. In particular they have been successfully applied to gene clustering and, very recently, also to samples classification. In this latter case, nevertheless, the basic assumption of functional independence between genes is limiting, since many other a priori information about genes’ interactions may be available (co-regulation, spatial proximity or other a priori knowledge). In this paper a novel topic model is proposed, which enriches and extends the Latent Dirichlet Allocation (LDA) model by integrating such dependencies, encoded in a categorization of genes. The proposed topic model is used to derive a highly informative and discriminant representation for microarray experiments. Its usefulness, in comparison with standard topic models, has been demonstrated in two different classification tests.KeywordsTopic ModelLatent Dirichlet AllocationMultinomial DistributionProbabilistic Latent Semantic AnalysisLatent Dirichlet Allocation ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Understanding and predicting the effect of global change phenomena on biodiversity is challenging given that biodiversity data are highly multivariate, containing information from tens to hundreds of species in any given location and time. The Latent Dirichlet Allocation (LDA) model has been recently proposed to decompose biodiversity data into latent communities. While LDA is a very useful exploratory tool and overcomes several limitations of earlier methods, it has limited inferential and predictive skill given that covariates cannot be included in the model. We introduce a modified LDA model (called LDAcov) which allows the incorporation of covariates, enabling inference on the drivers of change of latent communities, spatial interpolation of results, and prediction based on future environmental change scenarios. We show with simulated data that our approach to fitting LDAcov is able to estimate well the number of groups and all model parameters. We illustrate LDAcov using data from two experimental studies on the long‐term effects of fire on southeastern Amazonian forests in Brazil. Our results reveal that repeated fires can have a strong impact on plant assemblages, particularly if fuel is allowed to build up between consecutive fires. The effect of fire is exacerbated as distance to the edge of the forest decreases, with small‐sized species and species with thin bark being impacted the most. These results highlight the compounding impacts of multiple fire events and fragmentation, a scenario commonly found across the southern edge of Amazon. We believe that LDAcov will be of wide interest to scientists studying the effect of global change phenomena on biodiversity using high‐dimensional datasets. Thus, we developed the R package LDAcov to enable the straightforward use of this model.

How to recognize human action from videos captured by modern cameras efficiently and effectively is a challenge in real applications. Traditional methods which need professional analysts are facing a bottleneck because of their shortcomings. To cope with the disadvantage, methods based on computer vision techniques, without or with only a few human interventions, have been proposed to analyse human actions in videos automatically. This paper provides a method combining the three dimensional Scale Invariant Feature Transform (SIFT) detector and the Latent Dirichlet Allocation (LDA) model for human motion analysis. To represent videos effectively and robustly, we extract the 3D SIFT descriptor around each interest point, which is sampled densely from 3D Space-time video volumes. After obtaining the representation of each video frame, the LDA model is adopted to discover the underlying structure-the categorization of human actions in the collection of videos. Public available standard datasets are used to test our method. The concluding part discusses the research challenges and future directions.

Academic audit reports play an important role in assessing and monitoring the quality of higher education. However, most of these reports are arranged in an unstructured narrative descriptive form, making it difficult to analyze systematically and consistently, especially if done manually. This poses a challenge for auditors and decision makers in identifying patterns of findings and quality issues efficiently. This study aims to apply and evaluate the Latent Dirichlet Allocation (LDA) method in extracting keywords and abstracting main topics from academic audit report texts. The dataset was obtained from the Quality Management System (SIMUTU) of Surabaya State University, which includes hundreds of audit finding descriptions from various faculties over the past three years. The methodology used includes text preprocessing stages using tokenization, stopword removal, and stemming techniques, followed by topic modeling using LDA. Evaluation was carried out quantitatively using a coherence score to assess topic quality, and qualitatively through visualization of results in the form of word clouds and pyLDAvis. The results showed that the LDA model was able to produce meaningful, representative, and relevant topics in the context of academic quality, such as document management, lecturer involvement, and implementation of learning evaluations. Manual validation by internal quality experts showed that the generated topics can help in understanding audit findings trends more quickly and objectively. Thus, LDA has proven to be effective as an approach to extracting important information from unstructured audit reports and has great potential to be integrated into data-driven quality dashboard systems to support more informed and evidence-based decision making.

Objective: To analyze the literature on rational drug use in China from 2021 to June 2024, sort out the recent situation of rational drug use in China, and find out the potential problems of rational drug use in China, in order to further promote the standardized management of rational drug use. Methods: Based on the literature on rational drug use in CNKI, the title, abstract, keyword and discussion were taken as the research objects, the LDA model was used for text mining, and 304 articles were included after removing irrelevant literature, and 6 topics were mined by using the latent Dirichlet allocation (LDA)model, and the theme research trends were found for visual display. Results: By analyzing and summarizing relevant data, it provided a reference for optimizing rational drug use strategies.

In this paper, we propose a method for short text categorization using topic model and integrated classifier. To enrich the representation of short text, the Latent Dirichlet Allocation (LDA) model is used to extract latent topic information. While for classification, we combine two classifiers for achieving high reliability. Particularly, we train LDA models with variable number of topics using the Wikipedia corpus as external knowledge base, and extend labeled Web snippets by potential topics extracted by LDA. Then, the enriched representation of snippets are used to learn Maximum Entropy (MaxEnt) and support vector machine (SVM) classifiers separately. Finally, viewing that the most possible predicted result will appear in the top two candidates selected by MaxEnt classifier, we develop a novel scheme that if the gap between these candidates is large enough, the predicted result is considered to be reliable; otherwise, the SVM classifier will be integrated with MaxEnt classifier to make a comprehensive prediction. Experimental results show that our framework is effective and can outperform the state-of-the-art techniques.

To improve hydro-chemical modeling and forecasting, there is a need to better understand flood-induced variability in water chemistry and the processes controlling it in watersheds. In the literature, assumptions are often made, for instance, that stream chemistry reacts differently to rainfall events depending on the season; however, methods to verify such assumptions are not well developed. Often, few floods are studied at a time and chemicals are used as tracers. Grouping similar events from large multivariate datasets using principal component analysis and clustering methods helps to explain hydrological processes; however, these methods currently have some limits (definition of flood descriptors, linear assumption, for instance). Most clustering methods have been used in the context of regionalization, focusing more on mapping results than on understanding processes. In this study, we extracted flood patterns using the probabilistic Latent Dirichlet Allocation (LDA) model, its first use in hydrology, to our knowledge. The LDA method allows multivariate temporal datasets to be considered without having to define explanatory factors beforehand or select representative floods. We analyzed a multivariate dataset from a long-term observatory (Kervidy-Naizin, western France) containing data for four solutes monitored daily for 12 years: nitrate, chloride, dissolved organic carbon, and sulfate. The LDA method extracted four different patterns that were distributed by season. Each pattern can be explained by seasonal hydrological processes. Hydro-meteorological parameters help explain the processes leading to these patterns, which increases understanding of flood-induced variability in water quality. Thus, the LDA method appears useful for analyzing long-term datasets.

Majority of people now search the internet for news or information topics. The growth of the internet and social media has led to the emergence of hundreds of portals or online news with very diverse news topics. Searching for headlines manually is an ineffective and time-consuming method. In this study headlines modeling was used using Latent Dirichlet Allocation (LDA). Prior to the application of the LDA model, supporting processes such as tokenization, lemmatization, tf-idf factorization and non-negative matrix factorization were also applied. The results showed that the LDA can be applied to model the news topic well with a loglikelihood score of -13615,912 and a perplexity score of 378,958. In addition to using LDA, topic modeling is also done in the form of clusters by applying k-means clustering. With the elbow method, the ideal number of clusters for k-means clustering is 5 clusters and the silhouette performance is 0.62