With rapidly evolving media narratives, it has become increasingly critical to not just extract narratives from a given corpus but rather investigate, how they develop over time. While popular narrative extraction methods such as Large Language Models do well in capturing typical narrative elements or even the complex structure of a narrative, applying them to an entire corpus comes with obstacles, such as a high financial or computational cost. We propose a combination of the language understanding capabilities of Large Language Models with the large scale applicability of topic models to dynamically model narrative shifts across time using the Narrative Policy Framework. We apply a topic model and a corresponding change point detection method to find changes that concern a specific topic of interest. Using this model, we filter our corpus for documents that are particularly representative of that change and feed them into a Large Language Model that interprets the change that happened in an automated fashion and distinguishes between content and narrative shifts. We employ our pipeline on a corpus of The Wall Street Journal news paper articles from 2009 to 2023. Our findings indicate that a Large Language Model can efficiently extract a narrative shift if one exists at a given point in time, but does not perform as well when having to decide whether a shift in content or a narrative shift took place.
Topic models often fail to capture low-prevalence, domain-critical themes, so-called minority topics, such as mental health themes in online comments. While some existing methods can incorporate domain knowledge, such as expected topical content, methods allowing guidance may require overly detailed expected topics, hindering the discovery of topic divisions and variation. We propose a topic modeling solution via a specially constrained NMF. We incorporate a seed word list characterizing minority content of interest, but we do not require experts to pre-specify their division across minority topics. Through prevalence constraints on minority topics and seed word content across topics, we learn distinct data-driven minority topics as well as majority topics. The constrained NMF is fitted via Karush-Kuhn-Tucker (KKT) conditions with multiplicative updates. We outperform several baselines on synthetic data in terms of topic purity, normalized mutual information, and also evaluate topic quality using Jensen-Shannon divergence (JSD). We conduct a case study on YouTube vlog comments, analyzing viewer discussion of mental health content; our model successfully identifies and reveals this domain-relevant minority content.
Topic models are statistical tools that allow their users to gain qualitative and quantitative insights into the contents of textual corpora without the need for close reading. They can be applied in a wide range of settings from discourse analysis, through pretraining data curation, to text filtering. Topic models are typically parameter-rich, complex models, and interpreting these parameters can be challenging for their users. It is typical practice for users to interpret topics based on the top 10 highest ranking terms on a given topic. This list-of-words approach, however, gives users a limited and biased picture of the content of topics. Thoughtful user interface design and visualizations can help users gain a more complete and accurate understanding of topic models' output. While some visualization utilities do exist for topic models, these are typically limited to a certain type of topic model. We introduce topicwizard, a framework for model-agnostic topic model interpretation, that provides intuitive and interactive tools that help users examine the complex semantic relations between documents, words and topics learned by topic models.
Traditional topic models often struggle with contextual nuances and fail to adequately handle polysemy and rare words. This limitation typically results in topics that lack coherence and quality. Large Language Models (LLMs) can mitigate this issue by generating an initial set of topics. However, these raw topics frequently lack refinement and representativeness, which leads to redundancy without lexical similarity and reduced interpretability. This paper introduces HAMLET, a graph-driven architecture for cross-lingual healthcare topic modeling that uses LLMs. The proposed approach leverages neural-enhanced semantic fusion to refine the embeddings of topics generated by the LLM. Instead of relying solely on statistical co-occurrence or human interpretation to extract topics from a document corpus, this method introduces a topic embedding refinement that uses Bidirectional Encoder Representations from Transformers (BERT) and Graph Neural Networks (GNN). After topic generation, a hybrid technique that involves BERT and Sentence-BERT (SBERT) is employed for embedding. The topic representations are further refined using a GNN, which establishes connections between documents, topics, words, similar topics, and similar words. A novel method is introduced to compute similarities. Consequently, the topic embeddings are refined, and the top k topics are extracted. Experiments were conducted using two healthcare datasets, one in English and one in French, from which six sets were derived. The results demonstrate the effectiveness of HAMLET.
The Topics over Time (ToT) model captures thematic changes in timestamped datasets by explicitly modeling publication dates jointly with word co-occurrence patterns. However, ToT was not approached in a fully Bayesian fashion, a flaw that makes it susceptible to stability problems. To address this issue, we propose a fully Bayesian Topics over Time (BToT) model via the introduction of a conjugate prior to the Beta distribution. This prior acts as a regularization that prevents the online version of the algorithm from unstable updates when a topic is poorly represented in a mini-batch. The characteristics of this prior to the Beta distribution are studied here for the first time. Still, this model suffers from a difference in scale between the single-time observations and the multiplicity of words per document. A variation of BToT, Weighted Bayesian Topics over Time (WBToT), is proposed as a solution. In WBToT, publication dates are repeated a certain number of times per document, which balances the relative influence of words and timestamps along the inference process. We have tested our models on two datasets: a collection of over 200 years of US state-of-the-union (SOTU) addresses and a large-scale COVID-19 Twitter corpus of 10 million tweets. The results show that WBToT captures events better than Latent Dirichlet Allocation and other SOTA topic models like BERTopic: the median absolute deviation of the topic presence over time is reduced by $51\%$ and $34\%$, respectively. Our experiments also demonstrate the superior coherence of WBToT over BToT, which highlights the importance of balancing the time and word modalities. Finally, we illustrate the stability of the online optimization algorithm in WBToT, which allows the application of WBToT to problems that are intractable for standard ToT.
Negative sampling has emerged as an effective technique that enables deep learning models to learn better representations by introducing the paradigm of learn-to-compare. The goal of this approach is to add robustness to deep learning models to learn better representation by comparing the positive samples against the negative ones. Despite its numerous demonstrations in various areas of computer vision and natural language processing, a comprehensive study of the effect of negative sampling in an unsupervised domain like topic modeling has not been well explored. In this paper, we present a comprehensive analysis of the impact of different negative sampling strategies on neural topic models. We compare the performance of several popular neural topic models by incorporating a negative sampling technique in the decoder of variational autoencoder-based neural topic models. Experiments on four publicly available datasets demonstrate that integrating negative sampling into topic models results in significant enhancements across multiple aspects, including improved topic coherence, richer topic diversity, and more accurate document classification. Manual evaluations also indicate that the inclusion of negative sampling into neural topic models enhances the quality of the generated topics. These findings highlight the potential of negative sampling as a valuable tool for advancing the effectiveness of neural topic models.
An important aspect of text mining involves information retrieval in form of discovery of semantic themes (topics) from documents using topic modelling. While generative topic models like Latent Dirichlet Allocation (LDA) elegantly model topics as probability distributions and are useful in identifying latent topics from large document corpora with minimal supervision, they suffer from difficulty in topic interpretability and reduced performance in shorter texts. Here we propose a novel Multivariate Gaussian Topic modelling (MGD) approach. In this approach topics are presented as Multivariate Gaussian Distributions and documents as Gaussian Mixture Models. Using EM algorithm, the various constituent Multivariate Gaussian Distributions and their corresponding parameters are identified. Analysis of the parameters helps identify the keywords having the highest variance and mean contributions to the topic, and from these key-words topic annotations are carried out. This approach is first applied on a synthetic dataset to demonstrate the interpretability benefits vis-\`a-vis LDA. A real-world application of this topic model is demonstrated in analysis of risks and hazards at a petrochemical plant by applying the model on safety incident reports to identify the major latent hazards plaguing the plant. This model achieves a higher mean topic coherence of 0.436 vis-\`a-vis 0.294 for LDA.
Topic models are widely used for discovering latent thematic structures in large text corpora, yet traditional unsupervised methods often struggle to align with predefined conceptual domains. This paper introduces Seeded Poisson Factorization (SPF), a novel approach that extends the Poisson Factorization framework by incorporating domain knowledge through seed words. SPF enables a more interpretable and structured topic discovery by modifying the prior distribution of topic-specific term intensities, assigning higher initial rates to predefined seed words. The model is estimated using variational inference with stochastic gradient optimization, ensuring scalability to large datasets. We apply SPF to an Amazon customer feedback dataset, leveraging predefined product categories as guiding structures. Our evaluation demonstrates that SPF achieves superior classification performance compared to alternative guided topic models, particularly in terms of computational efficiency and predictive performance. Furthermore, robustness checks highlight SPF's ability to adaptively balance domain knowledge and data-driven topic discovery, even in cases of imperfect seed word selection. These results establish SPF as a powerful and scalable alternative for integrating expert knowledge into topic modeling, enhancing both interpretability and efficiency in real-world applications.
The performance of large language models (LLMs) is significantly affected by the quality and composition of their pre-training data, which is inherently diverse, spanning various domains, sources, and topics. Effectively integrating these heterogeneous data sources is crucial for optimizing LLM performance. Previous research has predominantly concentrated on domain-based data mixing, often neglecting the nuanced topic-level characteristics of the data. To address this gap, we propose a simple yet effective topic-based data mixing strategy that utilizes fine-grained topics generated through our topic modeling method, DataWeave. DataWeave employs a multi-stage clustering process to group semantically similar documents and utilizes LLMs to generate detailed topics, thereby facilitating a more nuanced understanding of dataset composition. Our strategy employs heuristic methods to upsample or downsample specific topics, which significantly enhances LLM performance on downstream tasks, achieving superior results compared to previous, more complex data mixing approaches. Furthermore, we confirm that the topics Science and Relationships are particularly effective, yielding the most substantial performance improvements. We will make our code and datasets publicly available.
A common use of NLP is to facilitate the understanding of large document collections, with a shift from using traditional topic models to Large Language Models. Yet the effectiveness of using LLM for large corpus understanding in real-world applications remains under-explored. This study measures the knowledge users acquire with unsupervised, supervised LLM-based exploratory approaches or traditional topic models on two datasets. While LLM-based methods generate more human-readable topics and show higher average win probabilities than traditional models for data exploration, they produce overly generic topics for domain-specific datasets that do not easily allow users to learn much about the documents. Adding human supervision to the LLM generation process improves data exploration by mitigating hallucination and over-genericity but requires greater human effort. In contrast, traditional. models like Latent Dirichlet Allocation (LDA) remain effective for exploration but are less user-friendly. We show that LLMs struggle to describe the haystack of large corpora without human help, particularly domain-specific data, and face scaling and hallucination limitations due to context length constraints. Dataset available at https://huggingface. co/datasets/zli12321/Bills.
Textual documents are commonly connected in a hierarchical graph structure where a central document links to others with an exponentially growing connectivity. Though Hyperbolic Graph Neural Networks (HGNNs) excel at capturing such graph hierarchy, they cannot model the rich textual semantics within documents. Moreover, text contents in documents usually discuss topics of different specificity. Hierarchical Topic Models (HTMs) discover such latent topic hierarchy within text corpora. However, most of them focus on the textual content within documents, and ignore the graph adjacency across interlinked documents. We thus propose a Hierarchical Graph Topic Modeling Transformer to integrate both topic hierarchy within documents and graph hierarchy across documents into a unified Transformer. Specifically, to incorporate topic hierarchy within documents, we design a topic tree and infer a hierarchical tree embedding for hierarchical topic modeling. To preserve both topic and graph hierarchies, we design our model in hyperbolic space and propose Hyperbolic Doubly Recurrent Neural Network, which models ancestral and fraternal tree structure. Both hierarchies are inserted into each Transformer layer to learn unified representations. Both supervised and unsupervised experiments verify the effectiveness of our model.
In today's rapidly evolving technological landscape, organizations face the challenge of integrating external insights into their decision-making processes to stay competitive. To address this issue, this study proposes a method that combines topic modeling, expert knowledge inputs, and reinforcement learning (RL) to enhance the detection of technological changes. The method has four main steps: (1) Build a relevant topic model, starting with textual data like documents and reports to find key themes. (2) Create aspect-based topic models. Experts use curated keywords to build models that showcase key domain-specific aspects. (3) Iterative analysis and RL driven refinement: We examine metrics such as topic magnitude, similarity, entropy shifts, and how models change over time. We optimize topic selection with RL. Our reward function balances the diversity and similarity of the topics. (4) Synthesis and operational integration: Each iteration provides insights. In the final phase, the experts check these insights and reach new conclusions. These conclusions are designed for use in the firm's operational processes. The application is tested by forecasting trends in quantum communication. Results demonstrate the method's effectiveness in identifying, ranking, and tracking trends that align with expert input, providing a robust tool for exploring evolving technological landscapes. This research offers a scalable and adaptive solution for organizations to make informed strategic decisions in dynamic environments.
Stopword removal is a critical stage in many Machine Learning methods but often receives little consideration, it interferes with the model visualizations and disrupts user confidence. Inappropriately chosen or hastily omitted stopwords not only lead to suboptimal performance but also significantly affect the quality of models, thus reducing the willingness of practitioners and stakeholders to rely on the output visualizations. This paper proposes a novel extraction method that provides a corpus-specific probabilistic estimation of stopword likelihood and an interactive visualization system to support their analysis. We evaluated our approach and interface using real-world data, a commonly used Machine Learning method (Topic Modelling), and a comprehensive qualitative experiment probing user confidence. The results of our work show that our system increases user confidence in the credibility of topic models by (1) returning reasonable probabilities, (2) generating an appropriate and representative extension of common stopword lists, and (3) providing an adjustable threshold for estimating and analyzing stopwords visually. Finally, we discuss insights, recommendations, and best practices to support practitioners while improving the output of Machine Learning methods and topic model visualizations with robust stopword analysis and removal.
Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management. Given that language impairments manifest early in NCD progression, visual-stimulated narrative (VSN)-based analysis offers a promising avenue for NCD detection. Current VSN-based NCD detection methods primarily focus on linguistic microstructures (e.g., pauses, lexical diversity), which are potentially linked to bottom-up (stimulus-driven) cognitive processing. While these features illuminate basic language abilities, the higher-order linguistic macrostructures (e.g., thematic or logical development), which may reflect top-down (concept-driven) cognitive abilities, remain underexplored. These patterns are crucial for NCD detection yet challenging to quantify due to their abstract and complex nature. To bridge this gap, we propose two novel dynamic macrostructural approaches: (1) Dynamic Topic Model (DTM) to track topic evolution over time, and (2) Text-Image Temporal Alignment Network (TITAN) to measure cross-modal consistency between speech and visual stimuli. Experimental results validated the efficiency of proposed approaches in NCD detection, with TITAN achieving superior performance both on the CU-MARVEL-RABBIT corpus (F1 = 0.7238) and the ADReSS corpus (F1 = 0.8889). The feature contribution analysis revealed that macrostructural features (e.g., topic variability, topic change rate, and topic consistency) constituted the most significant contributors in the model's decision pathways, outperforming investigated microstructural features. These findings underscore the critical role of macrostructural patterns in understanding cognitive impairment mechanisms in NCDs.
As short text data in native languages like Hindi increasingly appear in modern media, robust methods for topic modeling on such data have gained importance. This study investigates the performance of BERTopic in modeling Hindi short texts, an area that has been under-explored in existing research. Using contextual embeddings, BERTopic can capture semantic relationships in data, making it potentially more effective than traditional models, especially for short and diverse texts. We evaluate BERTopic using 6 different document embedding models and compare its performance against 8 established topic modeling techniques, such as Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), Latent Semantic Indexing (LSI), Additive Regularization of Topic Models (ARTM), Probabilistic Latent Semantic Analysis (PLSA), Embedded Topic Model (ETM), Combined Topic Model (CTM), and Top2Vec. The models are assessed using coherence scores across a range of topic counts. Our results reveal that BERTopic consistently outperforms other models in capturing coherent topics from short Hindi texts.
This study explores the application of topic modelling techniques Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA) on the Socrata dataset spanning from 1908 to 2009. Categorized by operator type (military, commercial, and private), the analysis identified key themes such as pilot error, mechanical failure, weather conditions, and training deficiencies. The study highlights the unique strengths of each method: LDA ability to uncover overlapping themes, NMF production of distinct and interpretable topics, and PLSA nuanced probabilistic insights despite interpretative complexity. Statistical analysis revealed that PLSA achieved a coherence score of 0.32 and a perplexity value of -4.6, NMF scored 0.34 and 37.1, while LDA achieved the highest coherence of 0.36 but recorded the highest perplexity at 38.2. These findings demonstrate the value of topic modelling in extracting actionable insights from unstructured aviation safety narratives, aiding in the identification of risk factors and areas for improvement across sectors. Future directions include integrating additional contextual variables, leveraging neural topic models, and enhancing aviation safety protocols. This research provides a foundation for advanced text-mining applications in aviation safety management.
This study introduces Bidirectional Topic Matching (BTM), a novel method for cross-corpus topic modeling that quantifies thematic overlap and divergence between corpora. BTM is a flexible framework that can incorporate various topic modeling approaches, including BERTopic, Top2Vec, and Latent Dirichlet Allocation (LDA). BTM employs a dual-model approach, training separate topic models for each corpus and applying them reciprocally to enable comprehensive cross-corpus comparisons. This methodology facilitates the identification of shared themes and unique topics, providing nuanced insights into thematic relationships. Validation against cosine similarity-based methods demonstrates the robustness of BTM, with strong agreement metrics and distinct advantages in handling outlier topics. A case study on climate news articles showcases BTM's utility, revealing significant thematic overlaps and distinctions between corpora focused on climate change and climate action. BTM's flexibility and precision make it a valuable tool for diverse applications, from political discourse analysis to interdisciplinary studies. By integrating shared and unique topic analyses, BTM offers a comprehensive framework for exploring thematic relationships, with potential extensions to multilingual and dynamic datasets. This work highlights BTM's methodological contributions and its capacity to advance discourse analysis across various domains.
Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised tool in this field. However, the predominant objective in NTMs, which aims to discover topics maximizing data likelihood, often lacks alignment with the central goals of data mining and knowledge discovery which is to reveal interpretable insights from large data repositories. Overemphasizing likelihood maximization without incorporating topic regularization can lead to an overly expansive latent space for topic modeling. In this paper, we present an innovative approach to NTMs that addresses this misalignment by introducing contrastive learning measures to assess topic interpretability. We propose a novel NTM framework, named ContraTopic, that integrates a differentiable regularizer capable of evaluating multiple facets of topic interpretability throughout the training process. Our regularizer adopts a unique topic-wise contrastive methodology, fostering both internal coherence within topics and clear external distinctions among them. Comprehensive experiments conducted on three diverse datasets demonstrate that our approach consistently produces topics with superior interpretability compared to state-of-the-art NTMs.
Recent works in clustering-based topic models perform well in monolingual topic identification by introducing a pipeline to cluster the contextualized representations. However, the pipeline is suboptimal in identifying topics across languages due to the presence of language-dependent dimensions (LDDs) generated by multilingual language models. To address this issue, we introduce a novel, SVD-based dimension refinement component into the pipeline of the clustering-based topic model. This component effectively neutralizes the negative impact of LDDs, enabling the model to accurately identify topics across languages. Our experiments on three datasets demonstrate that the updated pipeline with the dimension refinement component generally outperforms other state-of-the-art cross-lingual topic models.
Uncovering hidden topics from short texts is challenging for traditional and neural models due to data sparsity, which limits word co-occurrence patterns, and label sparsity, stemming from incomplete reconstruction targets. Although data aggregation offers a potential solution, existing neural topic models often overlook it due to time complexity, poor aggregation quality, and difficulty in inferring topic proportions for individual documents. In this paper, we propose a novel model, GloCOM (Global Clustering COntexts for Topic Models), which addresses these challenges by constructing aggregated global clustering contexts for short documents, leveraging text embeddings from pre-trained language models. GloCOM can infer both global topic distributions for clustering contexts and local distributions for individual short texts. Additionally, the model incorporates these global contexts to augment the reconstruction loss, effectively handling the label sparsity issue. Extensive experiments on short text datasets show that our approach outperforms other state-of-the-art models in both topic quality and document representations.