Online Latent Dirichlet Allocation Research Articles

Unveiling tourist typology: using online reviews and LDA to understand motivations for visiting Kyoto's prominent temples

Purpose This study aims to identify motivations for visiting Kyoto's prominent religious attractions using latent Dirichlet allocation (LDA) text analysis of online reviews; establish linkages between push motivational factors and pull factors of the religious sites, forming distinct tourist typologies; and suggest strategies for Kyoto's destination marketing based on the findings. Design/methodology/approach This study analyzed 37,772 TripAdvisor reviews for Kyoto's top 25 religious sites from the pre-pandemic period (March 2020). LDA topic modeling extracts 18 underlying thematic dimensions from the review texts. Axial coding of these dimensions revealed five distinct tourist motivation typologies. Findings Five motivation typologies emerged: cultural seekers drawn to Japan's unique heritage, nature lovers attracted by scenic landscapes, chrono-seasonal experiencers seeking distinct seasonal views, crowd-avoiders prioritizing less congested visits and city wanderers engaging in local activities. Practical implications The findings offer valuable guidance for destination marketers and managers in Kyoto, enabling the development of targeted strategies to enhance visitor experiences and manage overcrowding at popular religious sites. Originality/value This research provides novel insights into nonreligious tourists' motivations for visiting religious sites in a crowded destination. By identifying distinct motivation-based tourist typologies, the study informs strategies for enhancing visitor experiences tailored to diverse needs, contributing to tourism literature and practical destination management.

An Emotional Analysis of Korean Topics based on Social Media Big Data Clustering

An innovative approach is introduced in this paper to address the challenges in emotional topic interpretation and accuracy in emotional situation assessment. Utilizing large data from social media to improve the accuracy of emotional analysis in online debates, with a specific emphasis on Korean themes. The proposed solution, the Online Topic Emotion Recognition Model (OTSRM), builds upon the foundational Online Latent Dirichlet Allocation (OLDA) model. The OTSRM integrates the concept of emotion intensity and introduces an inventive emotion iteration framework to tackle these issues. Key innovations of the OTSRM include establishing an affective evolution channel by augmenting affective heritability using a β priori. Additionally, the model generates two critical distribution matrices: one for characteristic words and another for affective words, facilitating a deeper understanding of emotional context within topics. The relative entropy method is employed to discern emotional tones in textual content, calculating maximum emotion values for topic focus within adjacent time segments. Validation experiments using five diverse network event datasets and comparisons to mainstream models demonstrate the OTSRM's effectiveness with emotion recognition accuracy rates of 85.56% and 81.03%. The OTSRM represents significant progress in addressing challenges associated with emotional topic analysis and precise emotional dynamics assessment in Korean social media data.

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers.

Text makes up a large portion of network data because it is the vehicle for people's direct expression of emotions and opinions. How to analyze and mine these emotional text data has become a hot topic of concern in academia and industry in recent years. The online LDA (Latent Dirichlet Allocation) model is used in this paper to train the social hot topic data of professional migrant workers on the same time slice, and the subtopic evolution and intensity are obtained. The topic development is divided into four categories, and the classification model is created using SVM (Support Vector Machine). Instead of decision makers, a virtual human with sensibility and rationality is built using a hierarchical emotional cognitive model to solve multiobjective optimization problems interactively. It analyzes human body structure and emotional signals, and then combines them with visual and physiological signals to create multimodal emotional data. An example is used to demonstrate the effectiveness of the proposed model.

Evaluation of clustering and topic modeling methods over health-related tweets and emails

BackgroundInternet provides different tools for communicating with patients, such as social media (e.g., Twitter) and email platforms. These platforms provided new data sources to shed lights on patient experiences with health care and improve our understanding of patient-provider communication. Several existing topic modeling and document clustering methods have been adapted to analyze these new free-text data automatically. However, both tweets and emails are often composed of short texts; and existing topic modeling and clustering approaches have suboptimal performance on these short texts. Moreover, research over health-related short texts using these methods has become difficult to reproduce and benchmark, partially due to the absence of a detailed comparison of state-of-the-art topic modeling and clustering methods on these short texts. MethodsWe trained eight state-of- the-art topic modeling and clustering algorithms on short texts from two health-related datasets (tweets and emails): Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), LDA with Gibbs Sampling (GibbsLDA), Online LDA, Biterm Model (BTM), Online Twitter LDA, and Gibbs Sampling for Dirichlet Multinomial Mixture (GSDMM), as well as the k-means clustering algorithm with two different feature representations: TF-IDF and Doc2Vec. We used cluster validity indices to evaluate the performance of topic modeling and clustering: two internal indices (i.e. assessing the goodness of a clustering structure without external information) and five external indices (i.e. comparing the results of a cluster analysis to an externally known provided class labels). ResultsIn overall, for number of clusters (k) from 2 to 50, Online Twitter LDA and GSDMM achieved the best performance in terms of internal indices, while LSI and k-means with TF-IDF had the highest external indices. Also, of all tweets (N = 286, 971; HPV represents 94.6% of tweets and lynch syndrome represents 5.4%), for k = 2, most of the methods could respect this initial clustering distribution. However, we found model performance varies with the source of data and hyper-parameters such as the number of topics and the number of iterations used to train the models. We also conducted an error analysis using the Hamming loss metric, for which the poorest value was obtained by GSDMM on both datasets. ConclusionsResearchers hoping to group or classify health related short-text data can expect to select the most suitable topic modeling and clustering methods for their specific research questions. Therefore, we presented a comparison of the most common used topic modeling and clustering algorithms over two health-related, short-text datasets using both internal and external clustering validation indices. Internal indices suggested Online Twitter LDA and GSDMM as the best, while external indices suggested LSI and k-means with TF-IDF as the best. In summary, our work suggested researchers can improve their analysis of model performance by using a variety of metrics, since there is not a single best metric.

Telco User Activity Level Prediction with Massive Mobile Broadband Data

Telecommunication (telco) operators aim to provide users with optimized services and bandwidth in a timely manner. The goal is to increase user experience while retaining profit. To do this, knowing the changing behavior patterns of users through their activity levels in advance can be a great help for operators to adjust their management strategies and reduce operational risk. To achieve this goal, the operators can make use of knowledge discovered from telco’s historical mobile broadband (MBB) records to predict mobile access activity level at an early stage. In this article, we report our research in a real-world telco setting involving more than one million telco users. Our novel contribution includes representing users as documents containing a collection of changing spatiotemporal “words” that express user behavior. By extracting users’ space-time access records in MBB data, we use latent Dirichlet allocation (LDA) to learn user-specific compact topic features for user activity level prediction. We propose a scalable online expectation-maximization (OEM) algorithm that can scale LDA to massive MBB data, which is significantly faster than several state-of-the-art online LDA algorithms. Using these real-world MBB data, we confirm high performance in user activity level prediction. In addition, we show that the inferred topics indicate that future activity level anomalies correlate highly with early skewed bandwidth supply and demand relations. Thus, our prediction system can also guide the telco operators to balance the telecommunication network in terms of supply-demand relations, saving deployment costs and energy of cell towers in the future.

User segmentation in online advertising using latent Dirichlet allocation

User segmentation is one the most important problems in online advertiting. The use of online latent Dirichlet allocation model for analysing big datasets for this purpose is proposed in this paper. The relationship between the number of segments and segmentation quality metrics is analized and criteria for assigning ads to user segments are proposed.

Fast Online EM for Big Topic Modeling

The expectation-maximization (EM) algorithm can compute the maximum-likelihood (ML) or maximum a posterior (MAP) point estimate of the mixture models or latent variable models such as latent Dirichlet allocation (LDA), which has been one of the most popular probabilistic topic modeling methods in the past decade. However, batch EM has high time and space complexities to learn big LDA models from big data streams. In this paper, we present a fast online EM (FOEM) algorithm that infers the topic distribution from the previously unseen documents incrementally with constant memory requirements. Within the stochastic approximation framework, we show that FOEM can converge to the local stationary point of the LDA's likelihood function. By dynamic scheduling for the fast speed and parameter streaming for the low memory usage, FOEM is more efficient for some lifelong topic modeling tasks than the state-of-the-art online LDA algorithms to handle both big data and big models (aka, big topic modeling) on just a PC.