Unsupervised Topic Modeling Research Articles

Business aspects in focus, investor underreaction and return predictability

Overlap in business aspects serves as a proxy for firm relatedness. Employing an unsupervised topic modelling methodology from machine learning, we characterize the attention allocations of earnings conference call participants (corporate executives, financial analysts, and investors) over the topics discussed. We construct a novel topic similarity measure that captures incremental, difficult-to-observe, and time-varying firm relatedness. However, valuable information from topic peers is not incorporated into stock price in a timely fashion. A long-short strategy based on the returns of topic peers yields a monthly alpha of approximately 69 basis points. Furthermore, return predictability stems primarily from similar business models, customer management, and influential macroeconomic situations. Return predictability is more pronounced among focal firms with higher information complexities and arbitrage costs. Overall, this study provides a novel approach to automatically summarise firms' business aspects in focus and highlights the asset pricing implications of investors' underreactions to non-obvious and dynamic firm relatedness hidden in earnings conference calls.

Supervised and unsupervised learning models for pharmaceutical drug rating and classification using consumer generated reviews

Optimization of medication therapy depends on maximizing benefits and minimizing side effects of medications. This research showed how a joint approach using text mining, natural language processing, and machine learning can provide information for personalized and optimized medication therapy. Reviews on the benefits and side effects of prescription and over-the-counter medications were used to determine how well an integrated supervised and unsupervised learning could learn medication satisfaction. Supervised learning with naïve-Bayes, non-linear support vector machine with radial basis function kernels, and random forests with CART decision trees was measured by a micro-aggregated Matthews correlation coefficient and a macro-averaged F1 measure. Random forests outperformed support vector machines by almost 250% and naive-Bayes by 600% on the two evaluation metrics. All models did better with three rating levels, instead of five. Topic modeling and stacked cluster analysis were coupled with parts-of-speech tagging and text mining operations to establish a robust data preprocessing procedure to eliminate noisy features from the data. Unsupervised topic modeling and clustering represented an exploratory validation of how easy supervised classification would be. Well-defined latent topics were discovered including topics on “sleep quality”, “the opportunity to get back to work”, and “weight gain”. Overlapping clusters revealed that incorporating more information on social, demographic, or medical history variables could improve classifier performance. This research provided evidence that medication satisfaction can be learned with carefully designed joint supervised, unsupervised, and natural language learning techniques.

XAI Transformer based Approach for Interpreting Depressed and Suicidal User Behavior on Online Social Networks

Online social networks can be used for mental healthcare monitoring using Artificial Intelligence and Machine Learning techniques for detecting various mental health disorders and corresponding risk assessment. Recent research in this domain has primarily been focused on leveraging deep neural networks and various Transformer based Large Language Models, which have now become state-of-the-art for most natural language processing and computational linguistic tasks due to their unmatched prediction accuracy. Unlike conventional machine learning algorithms, these deep neural networks are black box architectures, where it is difficult to interpret and explain their predicted outcome. However, a black box classification outcome is insufficient for healthcare applications. Such systems will not be widely adopted and trusted by healthcare practitioners if they are not able to understand and explain the reasoning behind the predicted decisions made by an AI and ML based healthcare diagnostic system. The key objective of our research is to demonstrate the applications of model agnostic, post hoc surrogate XAI techniques for providing explainability to classification decisions of pretrained LLMs (Transformers) based mental healthcare diagnostic systems fine-tuned (or trained) to detect depressive and suicidal behavior using UGC from online social networks. For this, we have used the two most recent and popular techniques, SHAP and LIME. We have conducted extensive and in-depth experiments with four datasets and six pretrained LLMs, three of which have already been domain-adapted using mental health related datasets. We have also performed Few Shot Learning experiments with these three pretrained mental health domain-adapted LLMs. The results of qualitative and descriptive data analysis in this paper demonstrate that in order to build a comprehensive understanding of a person’s psychological state, emotion, and behavior and to discover the causes, symptoms, and triggers of mental health issues, it is essential to utilize eXplAInable (XAI) techniques with Transformer based LLMs (supervised). Alternatively, Transformer based unsupervised topic modeling technique BERTopic may be used for mental health risk monitoring and cause or symptom extraction when supervised training of LLMs is not feasible due to dataset annotation or availability challenges.

Opinion Summarization via Submodular Information Measures

This paper focuses on opinion summarization for constructing subjective and concise summaries representing essential opinions of online text reviews. As previous works rarely focus on the relationship between opinions, topics, and sentences, we propose a set of new requirements for Opinion-Topic-Sentence, which are essential for performing opinion summarization. We prove that Opinion-Topic-Sentence can be theoretically analyzed by submodular information measures. Thus, our proposed method can reduce redundant information, strengthen the relevance to given topics, and informatively represent the underlying emotional variations. While conventional methods require human-labeled topics for extractive summarization, we use unsupervised topic modeling methods to generate topic features. We propose four submodular functions and two optimization algorithms with proven performance bounds that can maximize opinion summarization's utility. An automatic evaluation metric, Topic-based Opinion Variance, is also derived to compensate for ROUGE-based metrics of opinion summarization evaluation. Four large, diversified, and representative corpora, OPOSUM, Opinosis, Yelp, and Amazon reviews, are used in our study. The results on these online review texts corroborate the efficacy of our proposed metric and framework.

Exploring topic models to discern cyber threats on Twitter: A case study on Log4Shell

Gathering information about cyber threats from various sources can help organisations improve proactive cyber defense and mitigate potential cyber attacks. Recently, Twitter has shown to be beneficial in providing timely Cyber Threat Intelligence (CTI) concerning cyber threats, software vulnerabilities and exploits. However, manually identifying and investigating useful insights, patterns, and trends from abundant unstructured tweets is difficult. This work proposes an end-to-end data-driven framework to collect, analyze, and monitor tweets using unsupervised topic modeling techniques. A novel visualization technique is also proposed to monitor the dynamic topic trends over time, offering an interpretable way to gain insights into a topic's lifecycle. A case study is conducted on the Log4shell vulnerability incident to demonstrate the applicability of the proposed framework. Experiments are carried out on a real-world Twitter dataset collected from 47 users within the CTI community. Results indicate that the proposed framework can discover emerging topics relevant to real-world cybersecurity incidents, with Log4Shell-related topics identified before the common public disclosure date by the National Vulnerability Database (NVD). This framework can expedite the data processing workflow and visualization for cyber threat analysis, enabling organizations to identify trends and patterns that can potentially indicate a security breach or attack.

A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications

BackgroundSubstance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications.MethodsWe collected Twitter data for four medications—fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall® (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication.ResultsThe quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities.ConclusionsNLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders.

Aware but not prepared: understanding situational awareness during the century flood in Germany in 2021

In July 2021, intense rainfall in parts of Western Europe was followed by unprecedented flash flooding. The flooding killed many people, mostly in Germany, which was subsequently blamed on a lack of preparation by either authorities or the broader populace. Knowledge about people's awareness of a hazard, its impacts and possible adaptation can help improve hazard management. We used social media data to assess situational awareness, sentiments and behaviour before, during and after the flood. We analysed nearly 58,000 German Twitter (now X) messages about the flood using machine learning-based unsupervised topic modelling. We showed that message frequency translated into four phases with message content suggesting sender priorities shifted in each phase of the flood. Besides messages with weather updates, correlated topics included ‘Solidarity, recovery and aid’ and ‘Grief and empathy’ (together 22% of all tweets) and a set of four topics about climate change attribution, extreme weather, long-term flood protection measures and politics (together 38% of all tweets). Many topics depended on the phase of the flood and on the distance from the affected areas. For those near the affected areas, tweets about evacuation (in peak phase) and damage assessment (in recovery phase) were particularly prominent. The high engagement on Twitter to seek weather information might indicate awareness of the extreme climatic conditions before the flood, but the severity was unexpected. Social media data provided unprecedent citizen-generated real-time information. We discuss how rapid automated analysis could contribute to disaster communication and risk mitigation.

Content analysis of psychological first aid training manuals via topic modelling

ABSTRACT Background: Psychological First Aid (PFA) is practiced worldwide. This practice in English is guided through a small collection of training manuals. Despite ubiquitous practice and formal training materials, little is known about what topics are covered and in what depth in these influential manuals. As such, we analyzed the topic structure of these training manuals. Objective: To model the PFA manuals’ topics with the goal of identifying a set of topics with recurrent themes and evaluating the extent to which each manual demonstrated those themes. Method: This machine learning study employed an unsupervised topic modelling design using Latent Dirichlet Allocation. The variables are (1) the distribution of a word across documents and (2) the distribution of a word across topics. The level of measurement for all variables is continuous. The unit of analysis is words. Preprocessing and data analysis were carried out using the Orange Data Mining Toolbox (Demšar et al., 2013). This programme is a Python GUI. Results: Results indicated a ten-topic structure to the universe of the English PFA training manuals. These topics were: (1) Refugees, (2) Orientation Activities, (3) Community-Based Applications, (4) PTSD & Other Psychological Issues, (5) Training Materials, (6) Specific Helper Instructions, (7) PFA Scholarship, (8) MHPSS, (9) General Curriculum, and (10) Australian Specific Delivery. The depth of discourse on each topic varied widely between manuals. Conclusions: The Academics of the PFA topic shows a strong representation of the corpus and suggests current training manuals have stayed true to its evidence-supported practice. The topic of Community-Based Applications strongly represents the corpus and suggests that training models incorporate community-based applications. The scientific foundation and practical implementation of the training guides are essential elements. Limitations and implications were also discussed.

Trends in bushfire related tweets during the Australian ‘Black Summer’ of 2019/20

Social media is widely used in emergencies, but the nature of the communication is poorly understood. We employed unsupervised topic modelling and sentiment analysis to analyse more than 80,000 Twitter tweets posted by users in Australia over a six-month period before, during and after the severe bushfires in 2019–2020, dubbed the ‘Black Summer’. While tweets about bushfire updates dominated, politics, donations and support, impacts and public opinion were also prominent themes. Social impacts were important in the early phase of the fires (Sept & Oct 2019) with health impacts discussed when the fires were most intense and ecological impacts becoming important in the recovery phase. Twitter users also talked about emergency responses, mainly evacuation, as the fires were starting, showing that Twitter played an important role in communicating advice to leave early to avoid harm. Although the bushfires caused death, destruction and disruption, the sentiment of tweets was balanced − 40% of all tweets were positive, 36% negative and the remainder neutral (24%). Sentiments shifted little over time, but some topics were strongly associated with the expression of negative sentiments. The unusual severity of the fires was attributed to climate change in the early and recovery phases but misinformation about arson as a cause briefly diverted attention from climate change in the middle. Twitter was used to express anger about a lack of action by the Australian government to address climate change. Unexpectedly, Twitter users from the most affected areas were more likely to post positive tweets than those further away, particular during recovery, suggesting both resilience and gratitude for support provided to them. Analysis of trend data on Twitter using machine learning has the potential to identify, in real time, whether appropriate messages are reaching and being disseminated effectively, provide early warning of potentially harmful misinformation and, if undertaken repeatedly, provide a metric against which responses to fire can be measured.

What makes a theme park experience less enjoyable? Evidence from online customer reviews of Disneyland China.

The aim of this study was to explore the causes of tourists' lesser enjoyment of theme parks through an unsupervised machine learning approach-structural topic modelling. Specifically, the research extracted a comprehensive list of discussion topics about the travel experience of tourists through the analysis of 112,000 customer reviews released by visitors to the Shanghai Disney Resort from 16 June 2016 to 4 March 2022. Then, we used sentiment analysis to distinguish positive and negative topics and to explore the relationship between tourists who buy different ticket types and sentiments in negative topics. The results show that problems such as "parking," "service attitude," "recommendation feeling," "experience comparison," and "entrance" may be the main reasons for an unpleasant experience. In addition, we also found that when tourists travel in groups (e.g., via family tickets), customers feel unhappy because of parking and fast track problems. Moreover, when tourists travel alone or with small groups, staff service attitudes, experience comparisons, and entrance processes are the sources of greater concern. Our findings can help theme park managers to better understand the expectations of tourists and take effective measures to tackle issues causing customer dissatisfaction, and they can also contribute to theme park studies in tourism management.

User feedback on the NHS test & Trace Service during COVID-19: The use of machine learning to analyse free-text data from 37,914 England adults

ObjectivesThe UK government's approach to the pandemic relies on a test, trace and isolate strategy, mainly implemented via the digital NHS Test & Trace Service. Feedback on user experience is central to the successful development of public-facing Services. As the situation dynamically changes and data accumulate, interpretation of feedback by humans becomes time-consuming and unreliable. The specific objectives were to 1) evaluate a human-in-the-loop machine learning technique based on structural topic modelling in terms of its Service ability in the analysis of vast volumes of free-text data, 2) generate actionable themes that can be used to increase user satisfaction of the Service. MethodsWe evaluated an unsupervised Topic Modelling approach, testing models with 5–40 topics and differing covariates. Two human coders conducted thematic analysis to interpret the topics. We identified a Structural Topic Model with 25 topics and metadata as covariates as the most appropriate for acquiring insights. ResultsResults from analysis of feedback by 37,914 users from May 2020 to March 2021 highlighted issues with the Service falling within three major themes: multiple contacts and incompatible contact method and incompatible contact method, confusion around isolation dates and tracing delays, complex and rigid system. ConclusionsStructural Topic Modelling coupled with thematic analysis was found to be an effective technique to rapidly acquire user insights. Topic modelling can be a quick and cost-effective method to provide high quality, actionable insights from free-text feedback to optimize public health Services.

Let’s talk about risk! Stock market effects of risk disclosure for European energy utilities

We analyze how risk reporting by European energy utilities is related to uncertainty about firms’ future prospects. Using an unsupervised machine learning topic model, we classify the content of the risk reports presented in the notes to the financial statements into different risk topics over the period from 2007 to 2017. We find that more risk reporting is related to lower idiosyncratic volatility and that this relation is especially evident for reporting about credit risk, risk management processes, economic risk, and accounting-related risk. We also find that the uncertainty-decreasing effect of risk disclosure extends to a positive relation between risk disclosure and firm value. Our study contributes to the call for more transparency in risk reporting and disclosure. Interestingly, we are unable to identify a climate-related risk topic, and further tests show only a rudimentary disclosure of climate-related risks. Combining the usefulness of the current risk disclosure regulation with the current lack of climate-related risk disclosures, we see good reasons for increased mandatory climate-related risk disclosures.

Thirty-six years of contributions to queueing systems: a content analysis, topic modeling, and graph-based exploration of research published in the QUESTA journal

We investigate the 36-year history of research published in the journal Queueing Systems: Theory and Applications (QUESTA), to uncover trends over time in the research topics and themes covered as well as in authorship, co-authorship, and institutional affiliation. Our analysis includes three different approaches. First, we conduct a content analysis of titles and abstracts using selected keywords to examine trends in the three themes of models, methods, and concepts applied in each article. Second, we employ unsupervised topic modeling to identify more hidden topics discussed in the journal. Finally, we analyze the co-authorship graph to identify trends in co-authorship and changes in collaboration practices between authors and their research institutions. Our findings reveal a persistent popularity of studies focused on the basic modeling of queues, queueing networks, and queueing systems. We also confirm an increase in collaboration among authors over time.

The application of machine learning to rural population migration research

AbstractMany rural areas experience population stagnation or decline from out‐migration with corresponding economic downturns. This is the case for the Northern Territory in Australia, a vast and sparsely populated jurisdiction. Its government has long sought to encourage stronger population growth but its population is young and highly transient, leading to high staff turn‐overs and challenges for industries and government to attract families and skilled workers. Place‐based factors such as job opportunities, access to essential services or environmental amenities influence satisfaction and migration decisions. The aim of this study was to understand why people might stay or move away through analysing responses to two open‐text questions on the best and worst aspect of living in the Northern Territory. Over 3500 valid responses were analysed using machine learning‐based unsupervised topic modelling which uncovered latent clusters. Forty‐four percent of positive aspects were clustered into lifestyle factors, while negative aspects clustered around high living costs and crime. Some aspects, such as the weather and distance to other places were discussed as both positive and negative aspects. Topics discussed by respondents could be directly related to their intention to leave the Northern Territory, and also to specific individual's demographic characteristics providing insights for policies focused on attracting and retaining population. The use of unsupervised text mining in population research is rare and this study verifies its use to deliver objective and nuanced results generated from a large qualitative data set.

Assessing Characteristics and Compliance of Online Delta-8 Tetrahydrocannabinol Product Sellers.

Introduction: The debate over the legal status of many cannabis- and hemp-derived products, including delta-8 tetrahydrocannabinol (THC), is in question. Although low concentrations of delta-8 THC are legal at the Federal level, many states have implemented their own regulations to both allow and restrict its use and sale. Of concern, sellers with unknown legal credentials have appeared online and are actively selling this product. Materials and Methods: We characterized the marketing, sale, and compliance of online delta-8 THC sellers using (1) data collected from the Twitter Application Programming Interface with delta-8 THC-related keywords; (2) unsupervised topic modeling using the Biterm Topic Model to identify clusters of tweets involved in marketing and selling; (3) inductive coding to identify marketing and selling characteristics; and (4) web forensics and simulated shopping to determine compliance with state restrictions for delta-8 THC sales. Results: In total, 110 unique hyperlinks associated with 7085 tweets that included marketing and selling activity for delta-8 THC were collected. From these links, we conducted simulated purchasing in January 2021 to identify compliant and noncompliant websites. Among the vendors, age verification was not found in over half of websites (59, 53.63%); 60 (54.55%) did not report a physical address; and 74 (65.45%) sold delta-8 products direct-to-consumer. Sixty-seven (90.54%) of detected vendors shipped delta-8 products to addresses in states that prohibit sales. Forty-three (64.18%) of Internet Protocol addresses were located within the United States; all others were international. Conclusion: Our analysis suggests that online storefronts are illegally selling and shipping cannabinoid derivatives to U.S. consumers. Further research is needed to understand downstream health and regulatory impacts from this unregulated access.

Discovering the Research Topics on Construction Safety and Health Using Semi-Supervised Topic Modeling

Safety and health have been one of the major issues in the construction industry worldwide for decades, and the relevant research has correspondingly drawn much attention in the academic field. Considering the expanding size and increasing heterogeneity of this research field, this paper proposes the topic modeling approach to cluster latent topics, extract coherent keywords, and discover evolving trends over the past three decades. Focusing on a total of 1984 articles published in 27 different journal sources until February 2023, this paper applied both unsupervised topic modeling techniques—Latent Dirichlet Allocation (LDA) and Correlation Explanation (CorEx)—and their semi-supervised versions—Guided LDA and Anchored CorEx. The evolving trends and inter-relationship of 15 research topics generated by the Anchored CorEx model (the best-performing model) were analyzed. Top-listed documents of major topics were analyzed to discuss their standalone research focuses. The results of this paper provided helpful insights and implications of existing research and offered potential guides for future research on construction safety and health by helping researchers (1) select research topics of interest and clearing decaying topics; (2) extract the top words of each research topic using systematic approaches; and (3) explore the interconnection of different research topics as well as their standalone focuses.

The first year of the Covid-19 pandemic through the lens of r/Coronavirus subreddit: an exploratory study.

This study looks at the content on Reddit's COVID-19 community, r/Coronavirus, to capture and understand the main themes and discussions around the global pandemic, and their evolution over the first year of the pandemic.It studies 356,690 submissions (posts) and 9,413,331 comments associated with the submissions, corresponding to the period of 20th January 2020 and 31st January 2021. On each of these datasets we carried out analysis based on lexical sentiment and topics generated from unsupervised topic modelling. The study found that negative sentiments show higher ratio in submissions while negative sentiments were of the same ratio as positive ones in the comments. Terms associated more positively or negatively were identified. Upon assessment of the upvotes and downvotes, this study also uncovered contentious topics, particularly "fake" or misleading news. Through topic modelling, 9 distinct topics were identified from submissions while 20 were identified from comments. Overall, this study provides a clear overview on the dominating topics and popular sentiments pertaining the pandemic during the first year. Our methodology provides an invaluable tool for governments and health decision makers and authorities to obtain a deeper understanding of the dominant public concerns and attitudes, which is vital for understanding, designing and implementing interventions for a global pandemic.

TopicStriKer: A topic kernels-powered approach for text classification

Topic models are unsupervised machine learning techniques that output clusters of “topics” represented as co-occurring words with their associated probability distributions. Topic modeling algorithms find latent themes from large document collections by understanding their context. On the other hand, string kernels are supervised machine-learning techniques that quantify string similarities without explicit string encoding. We propose TopicStriKer, a model combining the advantages of unsupervised topic modeling with supervised string kernels for text classification tasks. The co-occurring topic words per topic and topic proportions per document obtained are used to reduce the document corpus to a topic-word sequence. This reduced representation is then used for text classification with the aid of string kernels, significantly improving accuracy and reducing training time. Experiments on the bag-of-words kernel-based string embeddings using the proposed algorithm outperform the traditional text classification approaches. This work extensively compares string kernels with topic modeling on various performance metrics to establish our findings.

Multimodal Topic Modeling by Exploring Characteristics of Short Text Social Media

Millions of people post images and texts to express their feelings and point of views on social media everyday, especially on the short text social media such as Twitter or Weibo. As the images can provide important supplementary information for the text, many multimodal topic models have been developed to mine the topics from the multimodal social media content. We summarize three fundamental characteristics of the short text multimodal social media. The first is that the text of a short social media document generally belong to only one topic. The second is that the attached images can be relevant to multiple topics due to the rich information expressed in the images. The last is that although in most cases, text and images in social media posts are relevant, it should be noted that in a small number of cases, text and pictures are not relevant. However, most of the current multimodal topic models fail to model the these characteristics, and thus may produce low-quality topics. Based on these characteristics, we propose an unsupervised multimodal topic model SMMTM to model the short text multimodal social media documents. In the SMMTM model, only one topic is sampled for the the text while an image can belong to different topics. The correlation of the topics between the text and the images in a document are also formulated in an appropriate way. The experiments on three short text social media datasets with four evaluation metrics show the advantages of our model over the existing models.

Unsupervised Machine Learning for Managing Safety Accidents in Railway Stations

For both passenger and freight transportation, railroad operations must be dependable, accessible, maintained, and safe (RAMS). In many urban areas, railway stations risk and safety accidents represent an essential safety concern for daily operations. Moreover, the accidents lead to damage to market reputation, including injuries and anxiety among the people and costs. This stations under pressure caused by higher demand which consuming infrastructure and raised the safety administration consideration. To analysing these accidents and utilising the technology such AI methods to enhance safety, it is suggested to use unsupervised topic modelling for better understand the contributors to these extreme accidents. It is conducted to optimise Latent Dirichlet Allocation (LDA) for fatality accidents in the railway stations from textual data gathered RSSB including 1000 accidents in the UK railway station. This research describes using the machine learning topic method for systematic spot accident characteristics to enhance safety and risk management in the stations and provides advanced analysing. The study evaluates the efficacy of text by mining from accident history, gaining information, lesson learned and deeply coherent of the risk caused by assessing fatalities accidents for large and enduring scale. This Intelligent Text Analysis presents predictive accuracy for valuable accident information such as root causes and the hot spots in the railway stations. Further, the big data analytics ’ improvement results in an understanding of the accidents’ nature in ways not possible if a considerable amount of safety history and not through narrow domain analysis of the accident reports. This technology renders stand with high accuracy and a beneficial and extensive new era of AI applications in railway industry safety and other fields for safety applications.