Title stringlengths 14 179 | Authors stringlengths 6 464 | Abstract stringlengths 83 1.93k | entry_id stringlengths 32 34 | Date timestamp[ns, tz=UTC] | Categories stringlengths 5 168 | year int32 2.01k 2.02k |
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Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue | Nhat Tran, Diane Litman | Knowledge retrieval is one of the major challenges in building a knowledge-grounded dialogue system. A common method is to use a neural retriever with a distributed approximate nearest-neighbor database to quickly find the relevant knowledge sentences. In this work, we propose an approach that utilizes topic modeling o... | http://arxiv.org/abs/2405.04713v1 | 2024-05-07T23:32:32 | cs.IR | 2,024 |
Inferring Discussion Topics about Exploitation of Vulnerabilities from Underground Hacking Forums | Felipe Moreno-Vera | The increasing sophistication of cyber threats necessitates proactive measures to identify vulnerabilities and potential exploits. Underground hacking forums serve as breeding grounds for the exchange of hacking techniques and discussions related to exploitation. In this research, we propose an innovative approach usin... | http://arxiv.org/abs/2405.04561v1 | 2024-05-07T14:54:32 | cs.CR, cs.AI, cs.CY, cs.LG | 2,024 |
Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling | Maxim Ifergan, Renana Keydar, Omri Abend, Amit Pinchevski | The vast collection of Holocaust survivor testimonies presents invaluable historical insights but poses challenges for manual analysis. This paper leverages advanced Natural Language Processing (NLP) techniques to explore the USC Shoah Foundation Holocaust testimony corpus. By treating testimonies as structured questio... | http://arxiv.org/abs/2405.02650v1 | 2024-05-04T12:29:00 | cs.CL, cs.AI | 2,024 |
A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media | Ayaz Mehmood, Muhammad Tayyab Zamir, Muhammad Asif Ayub, Nasir Ahmad, Kashif Ahmad | Over the last decade, similar to other application domains, social media content has been proven very effective in disaster informatics. However, due to the unstructured nature of the data, several challenges are associated with disaster analysis in social media content. To fully explore the potential of social media c... | http://arxiv.org/abs/2405.00903v1 | 2024-05-01T23:19:49 | cs.CL | 2,024 |
Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling | Yida Mu, Peizhen Bai, Kalina Bontcheva, Xingyi Song | Large language models (LLMs) with their strong zero-shot topic extraction capabilities offer an alternative to probabilistic topic modelling and closed-set topic classification approaches. As zero-shot topic extractors, LLMs are expected to understand human instructions to generate relevant and non-hallucinated topics ... | http://arxiv.org/abs/2405.00611v1 | 2024-05-01T16:32:07 | cs.CL | 2,024 |
Unraveling the Italian and English Telegram Conspiracy Spheres through Message Forwarding | Lorenzo Alvisi, Serena Tardelli, Maurizio Tesconi | Telegram has grown into a significant platform for news and information sharing, favored for its anonymity and minimal moderation. This openness, however, makes it vulnerable to misinformation and conspiracy theories. In this study, we explore the dynamics of conspiratorial narrative dissemination within Telegram, focu... | http://arxiv.org/abs/2404.18602v1 | 2024-04-29T11:17:42 | cs.SI, F.2.2, I.2.7 | 2,024 |
Quantitative Tools for Time Series Analysis in Natural Language Processing: A Practitioners Guide | W. Benedikt Schmal | Natural language processing tools have become frequently used in social sciences such as economics, political science, and sociology. Many publications apply topic modeling to elicit latent topics in text corpora and their development over time. Here, most publications rely on visual inspections and draw inference on c... | http://arxiv.org/abs/2404.18499v1 | 2024-04-29T08:41:17 | econ.GN, q-fin.EC | 2,024 |
A Large-Scale Empirical Study of COVID-19 Contact Tracing Mobile App Reviews | Sifat Ishmam Parisa, Md Awsaf Alam Anindya, Anindya Iqbal, Gias Uddin | Since the beginning of 2020, the novel coronavirus has begun to sweep across the globe. Given the prevalence of smartphones everywhere, many countries across continents also developed COVID-19 contract tracing apps that users can install to get a warning of potential contacts with infected people. Unlike regular apps t... | http://arxiv.org/abs/2404.18125v1 | 2024-04-28T09:31:36 | cs.SE | 2,024 |
Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case | Muhammad Asif Auyb, Muhammad Tayyab Zamir, Imran Khan, Hannia Naseem, Nasir Ahmad, Kashif Ahmad | This paper focuses on a very important societal challenge of water quality analysis. Being one of the key factors in the economic and social development of society, the provision of water and ensuring its quality has always remained one of the top priorities of public authorities. To ensure the quality of water, differ... | http://arxiv.org/abs/2404.14977v1 | 2024-04-23T12:33:14 | cs.SI, cs.CL | 2,024 |
A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part II -- A Data Science Perspective | Mingyu Huang, Ke Li | This paper presents the second part of the two-part survey series on decomposition-based evolutionary multi-objective optimization where we mainly focus on discussing the literature related to multi-objective evolutionary algorithms based on decomposition (MOEA/D). Complementary to the first part, here we employ a seri... | http://arxiv.org/abs/2404.14228v1 | 2024-04-22T14:38:58 | cs.NE | 2,024 |
Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM | Michelle S. Lam, Janice Teoh, James Landay, Jeffrey Heer, Michael S. Bernstein | Data analysts have long sought to turn unstructured text data into meaningful concepts. Though common, topic modeling and clustering focus on lower-level keywords and require significant interpretative work. We introduce concept induction, a computational process that instead produces high-level concepts, defined by ex... | http://arxiv.org/abs/2404.12259v1 | 2024-04-18T15:26:02 | cs.HC, cs.AI | 2,024 |
Empowering Interdisciplinary Research with BERT-Based Models: An Approach Through SciBERT-CNN with Topic Modeling | Darya Likhareva, Hamsini Sankaran, Sivakumar Thiyagarajan | Researchers must stay current in their fields by regularly reviewing academic literature, a task complicated by the daily publication of thousands of papers. Traditional multi-label text classification methods often ignore semantic relationships and fail to address the inherent class imbalances. This paper introduces a... | http://arxiv.org/abs/2404.13078v2 | 2024-04-16T05:21:47 | cs.CL, cs.LG | 2,024 |
Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy | Tunazzina Islam, Dan Goldwasser | The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. Supervised methods are adept at text categorization, yet the dynamic nature of social media discussions poses a continual challenge for these techniques due to the constant shifting of the focus. On th... | http://arxiv.org/abs/2404.10259v1 | 2024-04-16T03:26:43 | cs.CL, cs.AI, cs.CY, cs.LG, cs.SI | 2,024 |
A solution for the mean parametrization of the von Mises-Fisher distribution | Marcel Nonnenmacher, Maneesh Sahani | The von Mises-Fisher distribution as an exponential family can be expressed in terms of either its natural or its mean parameters. Unfortunately, however, the normalization function for the distribution in terms of its mean parameters is not available in closed form, limiting the practicality of the mean parametrizatio... | http://arxiv.org/abs/2404.07358v1 | 2024-04-10T21:28:54 | stat.CO, stat.ML | 2,024 |
GINopic: Topic Modeling with Graph Isomorphism Network | Suman Adhya, Debarshi Kumar Sanyal | Topic modeling is a widely used approach for analyzing and exploring large document collections. Recent research efforts have incorporated pre-trained contextualized language models, such as BERT embeddings, into topic modeling. However, they often neglect the intrinsic informational value conveyed by mutual dependenci... | http://arxiv.org/abs/2404.02115v1 | 2024-04-02T17:18:48 | cs.CL, cs.LG | 2,024 |
Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation | Silvia García-Méndez, Francisco de Arriba-Pérez, Ana Barros-Vila, Francisco J. González-Castaño, Enrique Costa-Montenegro | Financial news items are unstructured sources of information that can be mined to extract knowledge for market screening applications. Manual extraction of relevant information from the continuous stream of finance-related news is cumbersome and beyond the skills of many investors, who, at most, can follow a few source... | http://arxiv.org/abs/2404.01338v1 | 2024-03-30T17:49:34 | cs.CL, cs.CE, cs.IR, cs.LG, q-fin.ST | 2,024 |
Dual Simplex Volume Maximization for Simplex-Structured Matrix Factorization | Maryam Abdolali, Giovanni Barbarino, Nicolas Gillis | Simplex-structured matrix factorization (SSMF) is a generalization of nonnegative matrix factorization, a fundamental interpretable data analysis model, and has applications in hyperspectral unmixing and topic modeling. To obtain identifiable solutions, a standard approach is to find minimum-volume solutions. By taking... | http://arxiv.org/abs/2403.20197v1 | 2024-03-29T14:19:26 | math.NA, cs.IR, cs.LG, cs.NA, eess.SP, stat.ML | 2,024 |
Enhanced Short Text Modeling: Leveraging Large Language Models for Topic Refinement | Shuyu Chang, Rui Wang, Peng Ren, Haiping Huang | Crafting effective topic models for brief texts, like tweets and news headlines, is essential for capturing the swift shifts in social dynamics. Traditional topic models, however, often fall short in accurately representing the semantic intricacies of short texts due to their brevity and lack of contextual data. In our... | http://arxiv.org/abs/2403.17706v1 | 2024-03-26T13:50:34 | cs.CL, cs.AI | 2,024 |
Decoding excellence: Mapping the demand for psychological traits of operations and supply chain professionals through text mining | S. Di Luozzo, A. Fronzetti Colladon, M. M. Schiraldi | The current study proposes an innovative methodology for the profiling of psychological traits of Operations Management (OM) and Supply Chain Management (SCM) professionals. We use innovative methods and tools of text mining and social network analysis to map the demand for relevant skills from a set of job description... | http://arxiv.org/abs/2403.17546v1 | 2024-03-26T09:51:43 | cs.CL, cs.SI, econ.GN, physics.soc-ph, q-fin.EC, I.2.7; J.4; H.4.0 | 2,024 |
An Empirical Study of ChatGPT-related projects on GitHub | Zheng Lin, Neng Zhang | As ChatGPT possesses powerful capabilities in natural language processing and code analysis, it has received widespread attention since its launch. Developers have applied its powerful capabilities to various domains through software projects which are hosted on the largest open-source platform (GitHub) worldwide. Simu... | http://arxiv.org/abs/2403.17437v1 | 2024-03-26T07:06:54 | cs.SE | 2,024 |
Neural Multimodal Topic Modeling: A Comprehensive Evaluation | Felipe González-Pizarro, Giuseppe Carenini | Neural topic models can successfully find coherent and diverse topics in textual data. However, they are limited in dealing with multimodal datasets (e.g., images and text). This paper presents the first systematic and comprehensive evaluation of multimodal topic modeling of documents containing both text and images. I... | http://arxiv.org/abs/2403.17308v1 | 2024-03-26T01:29:46 | cs.CL, cs.AI, cs.LG, I.2.7 | 2,024 |
A Mixed Method Study of DevOps Challenges | Minaoar Hossain Tanzil, Masud Sarker, Gias Uddin, Anindya Iqbal | Context: DevOps practices combine software development and IT operations. There is a growing number of DevOps related posts in popular online developer forum Stack Overflow (SO). While previous research analyzed SO posts related to build/release engineering, we are aware of no research that specifically focused on DevO... | http://arxiv.org/abs/2403.16436v1 | 2024-03-25T05:35:40 | cs.SE, cs.HC | 2,024 |
Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling | Yida Mu, Chun Dong, Kalina Bontcheva, Xingyi Song | Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents. However, classic topic modelling approaches (e.g., LDA) have certain drawbacks, such as the lack of semantic understanding and the presence of overlapping to... | http://arxiv.org/abs/2403.16248v2 | 2024-03-24T17:39:51 | cs.CL | 2,024 |
AllHands: Ask Me Anything on Large-scale Verbatim Feedback via Large Language Models | Chaoyun Zhang, Zicheng Ma, Yuhao Wu, Shilin He, Si Qin, Minghua Ma, Xiaoting Qin, Yu Kang, Yuyi Liang, Xiaoyu Gou, Yajie Xue, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang | Verbatim feedback constitutes a valuable repository of user experiences, opinions, and requirements essential for software development. Effectively and efficiently extracting valuable insights from such data poses a challenging task. This paper introduces Allhands , an innovative analytic framework designed for large-s... | http://arxiv.org/abs/2403.15157v2 | 2024-03-22T12:13:16 | cs.SE | 2,024 |
Uncovering Latent Themes of Messaging on Social Media by Integrating LLMs: A Case Study on Climate Campaigns | Tunazzina Islam, Dan Goldwasser | This paper introduces a novel approach to uncovering and analyzing themes in social media messaging. Recognizing the limitations of traditional topic-level analysis, which tends to capture only the overarching patterns, this study emphasizes the need for a finer-grained, theme-focused exploration. Conventional methods ... | http://arxiv.org/abs/2403.10707v1 | 2024-03-15T21:54:00 | cs.CL, cs.AI, cs.CY, cs.LG, cs.SI | 2,024 |
Automating the Information Extraction from Semi-Structured Interview Transcripts | Angelina Parfenova | This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. Given the labor-intensive nature of traditional qualitative analysis methods, such as coding, there exists a significant demand for tools that can facilitate the analysi... | http://arxiv.org/abs/2403.04819v1 | 2024-03-07T13:53:03 | cs.CL, cs.CY, cs.IR, cs.SI | 2,024 |
Membership Inference Attacks and Privacy in Topic Modeling | Nico Manzonelli, Wanrong Zhang, Salil Vadhan | Recent research shows that large language models are susceptible to privacy attacks that infer aspects of the training data. However, it is unclear if simpler generative models, like topic models, share similar vulnerabilities. In this work, we propose an attack against topic models that can confidently identify member... | http://arxiv.org/abs/2403.04451v1 | 2024-03-07T12:43:42 | cs.CR, cs.CL, cs.LG | 2,024 |
Does Documentation Matter? An Empirical Study of Practitioners' Perspective on Open-Source Software Adoption | Aaron Imani, Shiva Radmanesh, Iftekhar Ahmed, Mohammad Moshirpour | In recent years, open-source software (OSS) has become increasingly prevalent in developing software products. While OSS documentation is the primary source of information provided by the developers' community about a product, its role in the industry's adoption process has yet to be examined. We conducted semi-structu... | http://arxiv.org/abs/2403.03819v1 | 2024-03-06T16:06:08 | cs.SE | 2,024 |
Probabilistic Topic Modelling with Transformer Representations | Arik Reuter, Anton Thielmann, Christoph Weisser, Benjamin Säfken, Thomas Kneib | Topic modelling was mostly dominated by Bayesian graphical models during the last decade. With the rise of transformers in Natural Language Processing, however, several successful models that rely on straightforward clustering approaches in transformer-based embedding spaces have emerged and consolidated the notion of ... | http://arxiv.org/abs/2403.03737v1 | 2024-03-06T14:27:29 | cs.LG, cs.CL | 2,024 |
GPTopic: Dynamic and Interactive Topic Representations | Arik Reuter, Anton Thielmann, Christoph Weisser, Sebastian Fischer, Benjamin Säfken | Topic modeling seems to be almost synonymous with generating lists of top words to represent topics within large text corpora. However, deducing a topic from such list of individual terms can require substantial expertise and experience, making topic modelling less accessible to people unfamiliar with the particulariti... | http://arxiv.org/abs/2403.03628v1 | 2024-03-06T11:34:20 | cs.CL | 2,024 |
The Geometric Structure of Topic Models | Johannes Hirth, Tom Hanika | Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research and application, an in-depth analysis of topic models is still an open research topic. State-of-the... | http://arxiv.org/abs/2403.03607v1 | 2024-03-06T10:53:51 | cs.AI | 2,024 |
Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis | Latifah Almurqren, Ryan Hodgson, Alexandra Cristea | Sentiment analysis (SA) has been, and is still, a thriving research area. However, the task of Arabic sentiment analysis (ASA) is still underrepresented in the body of research. This study offers the first in-depth and in-breadth analysis of existing ASA studies of textual content and identifies their common themes, do... | http://arxiv.org/abs/2403.01921v1 | 2024-03-04T10:37:48 | cs.CL | 2,024 |
TopicDiff: A Topic-enriched Diffusion Approach for Multimodal Conversational Emotion Detection | Jiamin Luo, Jingjing Wang, Guodong Zhou | Multimodal Conversational Emotion (MCE) detection, generally spanning across the acoustic, vision and language modalities, has attracted increasing interest in the multimedia community. Previous studies predominantly focus on learning contextual information in conversations with only a few considering the topic informa... | http://arxiv.org/abs/2403.04789v2 | 2024-03-04T08:38:53 | cs.CL, cs.AI, cs.LG | 2,024 |
Topic Modeling Analysis of Aviation Accident Reports: A Comparative Study between LDA and NMF Models | Aziida Nanyonga, Hassan Wasswa, Graham Wild | Aviation safety is paramount in the modern world, with a continuous commitment to reducing accidents and improving safety standards. Central to this endeavor is the analysis of aviation accident reports, rich textual resources that hold insights into the causes and contributing factors behind aviation mishaps. This pap... | http://arxiv.org/abs/2403.04788v1 | 2024-03-04T01:41:07 | cs.CL, Topic Modeling, Aviation Safety, Aviation Accident Reports, Machine
Learning, LDA, NMF | 2,024 |
Using Text Embeddings for Deductive Qualitative Research at Scale in Physics Education | Tor Ole B. Odden, Halvor Tyseng, Jonas Timmann Mjaaland, Markus Fleten Kreutzer, Anders Malthe-Sørenssen | We propose a technique for performing deductive qualitative data analysis at scale on text-based data. Using a natural language processing technique known as text embeddings, we create vector-based representations of texts in a high-dimensional meaning space within which it is possible to quantify differences as vector... | http://arxiv.org/abs/2402.18087v1 | 2024-02-28T06:18:54 | physics.ed-ph, physics.data-an | 2,024 |
COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modeling | Baihan Lin, Djallel Bouneffouf, Yulia Landa, Rachel Jespersen, Cheryl Corcoran, Guillermo Cecchi | The therapeutic working alliance is a critical factor in predicting the success of psychotherapy treatment. Traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. In this paper, we present COMPASS, a novel framework to directly infer the therapeutic working allian... | http://arxiv.org/abs/2402.14701v1 | 2024-02-22T16:56:44 | cs.CL, cs.AI, cs.HC, cs.LG, q-bio.NC | 2,024 |
Topic Modeling as Multi-Objective Contrastive Optimization | Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Cong-Duy T Nguyen, See-Kiong Ng, Anh Tuan Luu | Recent representation learning approaches enhance neural topic models by optimizing the weighted linear combination of the evidence lower bound (ELBO) of the log-likelihood and the contrastive learning objective that contrasts pairs of input documents. However, document-level contrastive learning might capture low-leve... | http://arxiv.org/abs/2402.07577v2 | 2024-02-12T11:18:32 | cs.CL | 2,024 |
Understanding the Progression of Educational Topics via Semantic Matching | Tamador Alkhidir, Edmond Awad, Aamena Alshamsi | Education systems are dynamically changing to accommodate technological advances, industrial and societal needs, and to enhance students' learning journeys. Curriculum specialists and educators constantly revise taught subjects across educational grades to identify gaps, introduce new learning topics, and enhance the l... | http://arxiv.org/abs/2403.05553v1 | 2024-02-10T08:24:29 | cs.CY, cs.CL, cs.LG | 2,024 |
RankSum An unsupervised extractive text summarization based on rank fusion | A. Joshi, E. Fidalgo, E. Alegre, R. Alaiz-Rodriguez | In this paper, we propose Ranksum, an approach for extractive text summarization of single documents based on the rank fusion of four multi-dimensional sentence features extracted for each sentence: topic information, semantic content, significant keywords, and position. The Ranksum obtains the sentence saliency rankin... | http://arxiv.org/abs/2402.05976v1 | 2024-02-07T22:24:09 | cs.LG, cs.AI | 2,024 |
AlbNews: A Corpus of Headlines for Topic Modeling in Albanian | Erion Çano, Dario Lamaj | The scarcity of available text corpora for low-resource languages like Albanian is a serious hurdle for research in natural language processing tasks. This paper introduces AlbNews, a collection of 600 topically labeled news headlines and 2600 unlabeled ones in Albanian. The data can be freely used for conducting topic... | http://arxiv.org/abs/2402.04028v1 | 2024-02-06T14:24:28 | cs.CL, cs.AI, cs.LG | 2,024 |
Identifying Reasons for Contraceptive Switching from Real-World Data Using Large Language Models | Brenda Y. Miao, Christopher YK Williams, Ebenezer Chinedu-Eneh, Travis Zack, Emily Alsentzer, Atul J. Butte, Irene Y. Chen | Prescription contraceptives play a critical role in supporting women's reproductive health. With nearly 50 million women in the United States using contraceptives, understanding the factors that drive contraceptives selection and switching is of significant interest. However, many factors related to medication switchin... | http://arxiv.org/abs/2402.03597v1 | 2024-02-06T00:14:53 | cs.CL, cs.IR, cs.LG | 2,024 |
Comparison of Topic Modelling Approaches in the Banking Context | Bayode Ogunleye, Tonderai Maswera, Laurence Hirsch, Jotham Gaudoin, Teresa Brunsdon | Topic modelling is a prominent task for automatic topic extraction in many applications such as sentiment analysis and recommendation systems. The approach is vital for service industries to monitor their customer discussions. The use of traditional approaches such as Latent Dirichlet Allocation (LDA) for topic discove... | http://arxiv.org/abs/2402.03176v1 | 2024-02-05T16:43:53 | cs.IR, cs.AI, cs.LG, stat.CO, H.3.3 | 2,024 |
Multilingual transformer and BERTopic for short text topic modeling: The case of Serbian | Darija Medvecki, Bojana Bašaragin, Adela Ljajić, Nikola Milošević | This paper presents the results of the first application of BERTopic, a state-of-the-art topic modeling technique, to short text written in a morphologi-cally rich language. We applied BERTopic with three multilingual embed-ding models on two levels of text preprocessing (partial and full) to evalu-ate its performance ... | http://arxiv.org/abs/2402.03067v1 | 2024-02-05T14:59:29 | cs.CL, cs.AI | 2,024 |
Modified K-means with Cluster Assignment -- Application to COVID-19 Data | Shreyash Rawat, V. Vijayarajan, V. B. Surya Prasath | Text extraction is a highly subjective problem which depends on the dataset that one is working on and the kind of summarization details that needs to be extracted out. All the steps ranging from preprocessing of the data, to the choice of an optimal model for predictions, depends on the problem and the corpus at hand.... | http://arxiv.org/abs/2402.03380v1 | 2024-02-04T05:46:21 | cs.IR | 2,024 |
From PARIS to LE-PARIS: Toward Patent Response Automation with Recommender Systems and Collaborative Large Language Models | Jung-Mei Chu, Hao-Cheng Lo, Jieh Hsiang, Chun-Chieh Cho | In patent prosecution, timely and effective responses to Office Actions (OAs) are crucial for securing patents. However, past automation and artificial intelligence research have largely overlooked this aspect. To bridge this gap, our study introduces the Patent Office Action Response Intelligence System (PARIS) and it... | http://arxiv.org/abs/2402.00421v2 | 2024-02-01T08:37:13 | cs.CL, cs.HC, cs.IR, cs.LG | 2,024 |
Network-based Topic Structure Visualization | Yeseul Jeon, Jina Park, Ick Hoon Jin, Dongjun Chungc | In the real world, many topics are inter-correlated, making it challenging to investigate their structure and relationships. Understanding the interplay between topics and their relevance can provide valuable insights for researchers, guiding their studies and informing the direction of research. In this paper, we util... | http://arxiv.org/abs/2401.17855v1 | 2024-01-31T14:17:00 | stat.AP, cs.HC, cs.IR | 2,024 |
Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis | Zongxia Li, Andrew Mao, Daniel Stephens, Pranav Goel, Emily Walpole, Alden Dima, Juan Fung, Jordan Boyd-Graber | Topic models are a popular tool for understanding text collections, but their evaluation has been a point of contention. Automated evaluation metrics such as coherence are often used, however, their validity has been questioned for neural topic models (NTMs) and can overlook a models benefits in real world applications... | http://arxiv.org/abs/2401.16348v2 | 2024-01-29T17:54:04 | cs.CL, cs.CY, cs.HC | 2,024 |
CFTM: Continuous time fractional topic model | Kei Nakagawa, Kohei Hayashi, Yugo Fujimoto | In this paper, we propose the Continuous Time Fractional Topic Model (cFTM), a new method for dynamic topic modeling. This approach incorporates fractional Brownian motion~(fBm) to effectively identify positive or negative correlations in topic and word distribution over time, revealing long-term dependency or roughnes... | http://arxiv.org/abs/2402.01734v2 | 2024-01-29T08:07:41 | cs.CL, cs.LG, q-fin.CP, stat.AP | 2,024 |
A Survey on Neural Topic Models: Methods, Applications, and Challenges | Xiaobao Wu, Thong Nguyen, Anh Tuan Luu | Topic models have been prevalent for decades to discover latent topics and infer topic proportions of documents in an unsupervised fashion. They have been widely used in various applications like text analysis and context recommendation. Recently, the rise of neural networks has facilitated the emergence of a new resea... | http://arxiv.org/abs/2401.15351v1 | 2024-01-27T08:52:19 | cs.CL, cs.AI, cs.IR | 2,024 |
On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling | Xiaobao Wu, Fengjun Pan, Thong Nguyen, Yichao Feng, Chaoqun Liu, Cong-Duy Nguyen, Anh Tuan Luu | Hierarchical topic modeling aims to discover latent topics from a corpus and organize them into a hierarchy to understand documents with desirable semantic granularity. However, existing work struggles with producing topic hierarchies of low affinity, rationality, and diversity, which hampers document understanding. To... | http://arxiv.org/abs/2401.14113v2 | 2024-01-25T11:47:58 | cs.CL | 2,024 |
Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses | Hale Sirin, Tom Lippincott | We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods for finding and characterizing patterns in the output, and relating them to traditional scholarship ... | http://arxiv.org/abs/2401.13905v1 | 2024-01-25T02:50:03 | cs.CL | 2,024 |
Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face | Xinyu Yang, Weixin Liang, James Zou | Advances in machine learning are closely tied to the creation of datasets. While data documentation is widely recognized as essential to the reliability, reproducibility, and transparency of ML, we lack a systematic empirical understanding of current dataset documentation practices. To shed light on this question, here... | http://arxiv.org/abs/2401.13822v1 | 2024-01-24T21:47:13 | cs.LG, cs.AI | 2,024 |
Longitudinal Sentiment Topic Modelling of Reddit Posts | Fabian Nwaoha, Ziyad Gaffar, Ho Joon Chun, Marina Sokolova | In this study, we analyze texts of Reddit posts written by students of four major Canadian universities. We gauge the emotional tone and uncover prevailing themes and discussions through longitudinal topic modeling of posts textual data. Our study focuses on four years, 2020-2023, covering COVID-19 pandemic and after p... | http://arxiv.org/abs/2401.13805v1 | 2024-01-24T20:56:23 | cs.SI, cs.IR, I.2.7 | 2,024 |
ConceptThread: Visualizing Threaded Concepts in MOOC Videos | Zhiguang Zhou, Li Ye, Lihong Cai, Lei Wang, Yigang Wang, Yongheng Wang, Wei Chen, Yong Wang | Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. Online learners need to watch the whole course video on MOOC platforms to learn the underlying new knowledge, which is often tedious and time-consuming due to the lack of a quick overview of the covered knowledge and their ... | http://arxiv.org/abs/2401.11132v1 | 2024-01-20T06:03:44 | cs.HC | 2,024 |
An Information Retrieval and Extraction Tool for Covid-19 Related Papers | Marcos V. L. Pivetta | Background: The COVID-19 pandemic has caused severe impacts on health systems worldwide. Its critical nature and the increased interest of individuals and organizations to develop countermeasures to the problem has led to a surge of new studies in scientific journals. Objetive: We sought to develop a tool that incorpor... | http://arxiv.org/abs/2401.16430v1 | 2024-01-20T01:34:50 | cs.IR, cs.CL | 2,024 |
Combining topic modelling and citation network analysis to study case law from the European Court on Human Rights on the right to respect for private and family life | M. Mohammadi, L. M. Bruijn, M. Wieling, M. Vols | As legal case law databases such as HUDOC continue to grow rapidly, it has become essential for legal researchers to find efficient methods to handle such large-scale data sets. Such case law databases usually consist of the textual content of cases together with the citations between them. This paper focuses on case l... | http://arxiv.org/abs/2401.16429v1 | 2024-01-19T14:30:35 | cs.IR, cs.CL, cs.DL, cs.LG | 2,024 |
Landscape of Generative AI in Global News: Topics, Sentiments, and Spatiotemporal Analysis | Lu Xian, Lingyao Li, Yiwei Xu, Ben Zefeng Zhang, Libby Hemphill | Generative AI has exhibited considerable potential to transform various industries and public life. The role of news media coverage of generative AI is pivotal in shaping public perceptions and judgments about this significant technological innovation. This paper provides in-depth analysis and rich insights into the te... | http://arxiv.org/abs/2401.08899v1 | 2024-01-17T00:53:31 | cs.CY | 2,024 |
Topic Modelling: Going Beyond Token Outputs | Lowri Williams, Eirini Anthi, Laura Arman, Pete Burnap | Topic modelling is a text mining technique for identifying salient themes from a number of documents. The output is commonly a set of topics consisting of isolated tokens that often co-occur in such documents. Manual effort is often associated with interpreting a topic's description from such tokens. However, from a hu... | http://arxiv.org/abs/2401.12990v1 | 2024-01-16T16:05:54 | cs.CL, cs.LG | 2,024 |
Understanding Emotional Disclosure via Diary-keeping in Quarantine on Social Media | Yue Deng, Changyang He, Bo Li | Quarantine is a widely-adopted measure during health crises caused by highly-contagious diseases like COVID-19, yet it poses critical challenges to public mental health. Given this context, emotional disclosure on social media in the form of keeping a diary emerges as a popular way for individuals to express emotions a... | http://arxiv.org/abs/2401.07230v1 | 2024-01-14T08:31:08 | cs.HC, cs.SI | 2,024 |
The Pulse of Mood Online: Unveiling Emotional Reactions in a Dynamic Social Media Landscape | Siyi Guo, Zihao He, Ashwin Rao, Fred Morstatter, Jeffrey Brantingham, Kristina Lerman | The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using these data to understand social behavior is difficult due to heterogeneity of topics and events discussed in the highl... | http://arxiv.org/abs/2401.06275v1 | 2024-01-11T22:12:55 | cs.SI | 2,024 |
Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model | Jiaheng Xie, Ruicheng Liang, Yidong Chai, Yang Liu, Daniel Zeng | While short-form videos head to reshape the entire social media landscape, experts are exceedingly worried about their depressive impacts on viewers, as evidenced by medical studies. To prevent widespread consequences, platforms are eager to predict these videos' impact on viewers' mental health. Subsequently, they can... | http://arxiv.org/abs/2402.10045v3 | 2024-01-11T03:36:47 | cs.CV, cs.LG | 2,024 |
Probabilistic emotion and sentiment modelling of patient-reported experiences | Curtis Murray, Lewis Mitchell, Jonathan Tuke, Mark Mackay | This study introduces a novel methodology for modelling patient emotions from online patient experience narratives. We employed metadata network topic modelling to analyse patient-reported experiences from Care Opinion, revealing key emotional themes linked to patient-caregiver interactions and clinical outcomes. We de... | http://arxiv.org/abs/2401.04367v1 | 2024-01-09T05:39:20 | cs.CL | 2,024 |
Using Zero-shot Prompting in the Automatic Creation and Expansion of Topic Taxonomies for Tagging Retail Banking Transactions | Daniel de S. Moraes, Pedro T. C. Santos, Polyana B. da Costa, Matheus A. S. Pinto, Ivan de J. P. Pinto, Álvaro M. G. da Veiga, Sergio Colcher, Antonio J. G. Busson, Rafael H. Rocha, Rennan Gaio, Rafael Miceli, Gabriela Tourinho, Marcos Rabaioli, Leandro Santos, Fellipe Marques, David Favaro | This work presents an unsupervised method for automatically constructing and expanding topic taxonomies using instruction-based fine-tuned LLMs (Large Language Models). We apply topic modeling and keyword extraction techniques to create initial topic taxonomies and LLMs to post-process the resulting terms and create a ... | http://arxiv.org/abs/2401.06790v2 | 2024-01-08T00:27:16 | cs.CL, cs.AI | 2,024 |
German Text Embedding Clustering Benchmark | Silvan Wehrli, Bert Arnrich, Christopher Irrgang | This work introduces a benchmark assessing the performance of clustering German text embeddings in different domains. This benchmark is driven by the increasing use of clustering neural text embeddings in tasks that require the grouping of texts (such as topic modeling) and the need for German resources in existing ben... | http://arxiv.org/abs/2401.02709v1 | 2024-01-05T08:42:45 | cs.CL, cs.AI | 2,024 |
Text mining arXiv: a look through quantitative finance papers | Michele Leonardo Bianchi | This paper explores articles hosted on the arXiv preprint server with the aim to uncover valuable insights hidden in this vast collection of research. Employing text mining techniques and through the application of natural language processing methods, we examine the contents of quantitative finance papers posted in arX... | http://arxiv.org/abs/2401.01751v2 | 2024-01-03T14:06:06 | cs.DL, cs.IR, q-fin.GN | 2,024 |
A Latent Dirichlet Allocation (LDA) Semantic Text Analytics Approach to Explore Topical Features in Charity Crowdfunding Campaigns | Prathamesh Muzumdar, George Kurian, Ganga Prasad Basyal | Crowdfunding in the realm of the Social Web has received substantial attention, with prior research examining various aspects of campaigns, including project objectives, durations, and influential project categories for successful fundraising. These factors are crucial for entrepreneurs seeking donor support. However, ... | http://arxiv.org/abs/2401.02988v1 | 2024-01-03T09:17:46 | cs.CL, stat.AP | 2,024 |
Discovering Significant Topics from Legal Decisions with Selective Inference | Jerrold Soh | We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalised regressions and post-selection significance tests. The method identifies case topics significantly correlated with outcomes, topic-word distribut... | http://arxiv.org/abs/2401.01068v1 | 2024-01-02T07:00:24 | cs.CL, cs.AI | 2,024 |
Recent Advances in Text Analysis | Zheng Tracy Ke, Pengsheng Ji, Jiashun Jin, Wanshan Li | Text analysis is an interesting research area in data science and has various applications, such as in artificial intelligence, biomedical research, and engineering. We review popular methods for text analysis, ranging from topic modeling to the recent neural language models. In particular, we review Topic-SCORE, a sta... | http://arxiv.org/abs/2401.00775v2 | 2024-01-01T14:41:10 | stat.AP, cs.IR | 2,024 |
AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining | Boshko Koloski, Nada Lavrač, Bojan Cestnik, Senja Pollak, Blaž Škrlj, Andrej Kastrin | In an era marked by a rapid increase in scientific publications, researchers grapple with the challenge of keeping pace with field-specific advances. We present the `AHAM' methodology and a metric that guides the domain-specific \textbf{adapt}ation of the BERTopic topic modeling framework to improve scientific text ana... | http://arxiv.org/abs/2312.15784v1 | 2023-12-25T18:23:03 | cs.CL, cs.AI | 2,023 |
Inference of Dependency Knowledge Graph for Electronic Health Records | Zhiwei Xu, Ziming Gan, Doudou Zhou, Shuting Shen, Junwei Lu, Tianxi Cai | The effective analysis of high-dimensional Electronic Health Record (EHR) data, with substantial potential for healthcare research, presents notable methodological challenges. Employing predictive modeling guided by a knowledge graph (KG), which enables efficient feature selection, can enhance both statistical efficien... | http://arxiv.org/abs/2312.15611v1 | 2023-12-25T04:45:36 | stat.ME, stat.ML | 2,023 |
Deep de Finetti: Recovering Topic Distributions from Large Language Models | Liyi Zhang, R. Thomas McCoy, Theodore R. Sumers, Jian-Qiao Zhu, Thomas L. Griffiths | Large language models (LLMs) can produce long, coherent passages of text, suggesting that LLMs, although trained on next-word prediction, must represent the latent structure that characterizes a document. Prior work has found that internal representations of LLMs encode one aspect of latent structure, namely syntax; he... | http://arxiv.org/abs/2312.14226v1 | 2023-12-21T16:44:39 | cs.CL, cs.AI, cs.LG, stat.ML, I.2.6; I.2.7 | 2,023 |
MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records | Yixuan Li, Archer Y. Yang, Ariane Marelli, Yue Li | Survival models can help medical practitioners to evaluate the prognostic importance of clinical variables to patient outcomes such as mortality or hospital readmission and subsequently design personalized treatment regimes. Electronic Health Records (EHRs) hold the promise for large-scale survival analysis based on sy... | http://arxiv.org/abs/2312.13454v3 | 2023-12-20T22:13:45 | cs.LG, stat.ME | 2,023 |
Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets | Nirmalya Thakur, Yuvraj Nihal Duggal, Zihui Liu | The recent outbreak of the MPox virus has resulted in a tremendous increase in the usage of Twitter. Prior works in this area of research have primarily focused on the sentiment analysis and content analysis of these Tweets, and the few works that have focused on topic modeling have multiple limitations. This paper aim... | http://arxiv.org/abs/2312.11895v1 | 2023-12-19T06:39:38 | cs.SI, cs.AI, cs.CL, cs.CY | 2,023 |
Dynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notes | Hanwen Ye, Tatiana Moreno, Adrianne Alpern, Louis Ehwerhemuepha, Annie Qu | Mental health diseases affect children's lives and well-beings which have received increased attention since the COVID-19 pandemic. Analyzing psychiatric clinical notes with topic models is critical to evaluate children's mental status over time. However, few topic models are built for longitudinal settings, and they f... | http://arxiv.org/abs/2312.14180v1 | 2023-12-19T00:36:53 | cs.CL, cs.LG, stat.AP, stat.ML | 2,023 |
Topic-VQ-VAE: Leveraging Latent Codebooks for Flexible Topic-Guided Document Generation | YoungJoon Yoo, Jongwon Choi | This paper introduces a novel approach for topic modeling utilizing latent codebooks from Vector-Quantized Variational Auto-Encoder~(VQ-VAE), discretely encapsulating the rich information of the pre-trained embeddings such as the pre-trained language model. From the novel interpretation of the latent codebooks and embe... | http://arxiv.org/abs/2312.11532v2 | 2023-12-15T15:01:10 | cs.CL, cs.AI, cs.LG | 2,023 |
Prompting Large Language Models for Topic Modeling | Han Wang, Nirmalendu Prakash, Nguyen Khoi Hoang, Ming Shan Hee, Usman Naseem, Roy Ka-Wei Lee | Topic modeling is a widely used technique for revealing underlying thematic structures within textual data. However, existing models have certain limitations, particularly when dealing with short text datasets that lack co-occurring words. Moreover, these models often neglect sentence-level semantics, focusing primaril... | http://arxiv.org/abs/2312.09693v1 | 2023-12-15T11:15:05 | cs.AI, I.2.7 | 2,023 |
Topic Bias in Emotion Classification | Maximilian Wegge, Roman Klinger | Emotion corpora are typically sampled based on keyword/hashtag search or by asking study participants to generate textual instances. In any case, these corpora are not uniform samples representing the entirety of a domain. We hypothesize that this practice of data acquisition leads to unrealistic correlations between o... | http://arxiv.org/abs/2312.09043v3 | 2023-12-14T15:40:27 | cs.CL | 2,023 |
Contrastive News and Social Media Linking using BERT for Articles and Tweets across Dual Platforms | Jan Piotrowski, Marek Wachnicki, Mateusz Perlik, Jakub Podolak, Grzegorz Rucki, Michał Brzozowski, Paweł Olejnik, Julian Kozłowski, Tomasz Nocoń, Jakub Kozieł, Stanisław Giziński, Piotr Sankowski | X (formerly Twitter) has evolved into a contemporary agora, offering a platform for individuals to express opinions and viewpoints on current events. The majority of the topics discussed on Twitter are directly related to ongoing events, making it an important source for monitoring public discourse. However, linking tw... | http://arxiv.org/abs/2312.07599v1 | 2023-12-11T13:38:16 | cs.CL, cs.LG, I.2.7 | 2,023 |
PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language Models | Nirmalendu Prakash, Han Wang, Nguyen Khoi Hoang, Ming Shan Hee, Roy Ka-Wei Lee | The proliferation of social media has given rise to a new form of communication: memes. Memes are multimodal and often contain a combination of text and visual elements that convey meaning, humor, and cultural significance. While meme analysis has been an active area of research, little work has been done on unsupervis... | http://arxiv.org/abs/2312.06093v1 | 2023-12-11T03:36:50 | cs.CL, cs.CV, cs.MM, I.1.4; I.1.7 | 2,023 |
Revisiting Topic-Guided Language Models | Carolina Zheng, Keyon Vafa, David M. Blei | A recent line of work in natural language processing has aimed to combine language models and topic models. These topic-guided language models augment neural language models with topic models, unsupervised learning methods that can discover document-level patterns of word use. This paper compares the effectiveness of t... | http://arxiv.org/abs/2312.02331v1 | 2023-12-04T20:33:24 | cs.CL, cs.LG | 2,023 |
Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models | Chenguang Wang, Davis Engler, Xuechun Li, James Hou, David J. Wald, Kishor Jaiswal, Susu Xu | When a damaging earthquake occurs, immediate information about casualties is critical for time-sensitive decision-making by emergency response and aid agencies in the first hours and days. Systems such as Prompt Assessment of Global Earthquakes for Response (PAGER) by the U.S. Geological Survey (USGS) were developed to... | http://arxiv.org/abs/2312.03755v1 | 2023-12-04T17:09:58 | cs.CL, cs.AI, cs.CY, cs.LG | 2,023 |
Cybersecurity threats in FinTech: A systematic review | Danial Javaheri, Mahdi Fahmideh, Hassan Chizari, Pooia Lalbakhsh, Junbeom Hur | The rapid evolution of the Smart-everything movement and Artificial Intelligence (AI) advancements have given rise to sophisticated cyber threats that traditional methods cannot counteract. Cyber threats are extremely critical in financial technology (FinTech) as a data-centric sector expected to provide 24/7 services.... | http://arxiv.org/abs/2312.01752v1 | 2023-12-04T09:25:54 | cs.CR, cs.AI | 2,023 |
Understanding Opinions Towards Climate Change on Social Media | Yashaswi Pupneja, Joseph Zou, Sacha Lévy, Shenyang Huang | Social media platforms such as Twitter (now known as X) have revolutionized how the public engage with important societal and political topics. Recently, climate change discussions on social media became a catalyst for political polarization and the spreading of misinformation. In this work, we aim to understand how re... | http://arxiv.org/abs/2312.01217v1 | 2023-12-02T20:02:34 | cs.SI, cs.CL, cs.LG | 2,023 |
From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews | Alex Liu, Min Sun | Obtaining stakeholders' diverse experiences and opinions about current policy in a timely manner is crucial for policymakers to identify strengths and gaps in resource allocation, thereby supporting effective policy design and implementation. However, manually coding even moderately sized interview texts or open-ended ... | http://arxiv.org/abs/2312.01202v1 | 2023-12-02T18:55:14 | cs.HC, cs.AI, cs.CL | 2,023 |
Use of explicit replies as coordination mechanisms in online student debate | Bruno D. Ferreira-Saraiva, Joao P. Matos-Carvalho, Manuel Pita | People in conversation entrain their linguistic behaviours through spontaneous alignment mechanisms [7] - both in face-to-face and computer-mediated communication (CMC) [8]. In CMC, one of the mechanisms through which linguistic entrainment happens is through explicit replies. Indeed, the use of explicit replies influe... | http://arxiv.org/abs/2311.18466v1 | 2023-11-30T11:18:45 | cs.CL, cs.CY, cs.SI | 2,023 |
Public sentiment analysis and topic modeling regarding ChatGPT in mental health on Reddit: Negative sentiments increase over time | Yunna Cai, Fan Wang, Haowei Wang, Qianwen Qian | In order to uncover users' attitudes towards ChatGPT in mental health, this study examines public opinions about ChatGPT in mental health discussions on Reddit. Researchers used the bert-base-multilingual-uncased-sentiment techniques for sentiment analysis and the BERTopic model for topic modeling. It was found that ov... | http://arxiv.org/abs/2311.15800v1 | 2023-11-27T13:23:11 | cs.CY | 2,023 |
Searching for Snippets of Open-Domain Dialogue in Task-Oriented Dialogue Datasets | Armand Stricker, Patrick Paroubek | Most existing dialogue corpora and models have been designed to fit into 2 predominant categories : task-oriented dialogues portray functional goals, such as making a restaurant reservation or booking a plane ticket, while chit-chat/open-domain dialogues focus on holding a socially engaging talk with a user. However, h... | http://arxiv.org/abs/2311.14076v1 | 2023-11-23T16:08:39 | cs.CL | 2,023 |
Artificial Intelligence in the Service of Entrepreneurial Finance: Knowledge Structure and the Foundational Algorithmic Paradigm | Robert Kudelić, Tamara Šmaguc, Sherry Robinson | While the application of Artificial Intelligence in Finance has a long tradition, its potential in Entrepreneurship has been intensively explored only recently. In this context, Entrepreneurial Finance is a particularly fertile ground for future Artificial Intelligence proliferation. To support the latter, the study pr... | http://arxiv.org/abs/2311.13213v1 | 2023-11-22T07:58:46 | cs.AI | 2,023 |
Hate speech and hate crimes: a data-driven study of evolving discourse around marginalized groups | Malvina Bozhidarova, Jonathn Chang, Aaishah Ale-rasool, Yuxiang Liu, Chongyao Ma, Andrea L. Bertozzi, P. Jeffrey Brantingham, Junyuan Lin, Sanjukta Krishnagopal | This study explores the dynamic relationship between online discourse, as observed in tweets, and physical hate crimes, focusing on marginalized groups. Leveraging natural language processing techniques, including keyword extraction and topic modeling, we analyze the evolution of online discourse after events affecting... | http://arxiv.org/abs/2311.11163v1 | 2023-11-18T20:49:15 | cs.SI, stat.AP, stat.CO | 2,023 |
Labeled Interactive Topic Models | Kyle Seelman, Mozhi Zhang, Jordan Boyd-Graber | Topic models are valuable for understanding extensive document collections, but they don't always identify the most relevant topics. Classical probabilistic and anchor-based topic models offer interactive versions that allow users to guide the models towards more pertinent topics. However, such interactive features hav... | http://arxiv.org/abs/2311.09438v2 | 2023-11-15T23:18:01 | cs.LG, cs.CL, cs.HC, cs.IR | 2,023 |
Multi-Label Topic Model for Financial Textual Data | Moritz Scherrmann | This paper presents a multi-label topic model for financial texts like ad-hoc announcements, 8-K filings, finance related news or annual reports. I train the model on a new financial multi-label database consisting of 3,044 German ad-hoc announcements that are labeled manually using 20 predefined, economically motivate... | http://arxiv.org/abs/2311.07598v1 | 2023-11-10T12:56:07 | q-fin.ST, cs.CL, cs.LG | 2,023 |
Profiling Irony & Stereotype: Exploring Sentiment, Topic, and Lexical Features | Tibor L. R. Krols, Marie Mortensen, Ninell Oldenburg | Social media has become a very popular source of information. With this popularity comes an interest in systems that can classify the information produced. This study tries to create such a system detecting irony in Twitter users. Recent work emphasize the importance of lexical features, sentiment features and the cont... | http://arxiv.org/abs/2311.04885v1 | 2023-11-08T18:44:47 | cs.CL | 2,023 |
Topic model based on co-occurrence word networks for unbalanced short text datasets | Chengjie Ma, Junping Du, Meiyu Liang, Zeli Guan | We propose a straightforward solution for detecting scarce topics in unbalanced short-text datasets. Our approach, named CWUTM (Topic model based on co-occurrence word networks for unbalanced short text datasets), Our approach addresses the challenge of sparse and unbalanced short text topics by mitigating the effects ... | http://arxiv.org/abs/2311.02566v1 | 2023-11-05T04:44:23 | cs.CL | 2,023 |
TopicGPT: A Prompt-based Topic Modeling Framework | Chau Minh Pham, Alexander Hoyle, Simeng Sun, Philip Resnik, Mohit Iyyer | Topic modeling is a well-established technique for exploring text corpora. Conventional topic models (e.g., LDA) represent topics as bags of words that often require "reading the tea leaves" to interpret; additionally, they offer users minimal control over the formatting and specificity of resulting topics. To tackle t... | http://arxiv.org/abs/2311.01449v2 | 2023-11-02T17:57:10 | cs.CL | 2,023 |
Software Repositories and Machine Learning Research in Cyber Security | Mounika Vanamala, Keith Bryant, Alex Caravella | In today's rapidly evolving technological landscape and advanced software development, the rise in cyber security attacks has become a pressing concern. The integration of robust cyber security defenses has become essential across all phases of software development. It holds particular significance in identifying criti... | http://arxiv.org/abs/2311.00691v1 | 2023-11-01T17:46:07 | cs.SE, cs.CR, cs.LG | 2,023 |
Federated Topic Model and Model Pruning Based on Variational Autoencoder | Chengjie Ma, Yawen Li, Meiyu Liang, Ang Li | Topic modeling has emerged as a valuable tool for discovering patterns and topics within large collections of documents. However, when cross-analysis involves multiple parties, data privacy becomes a critical concern. Federated topic modeling has been developed to address this issue, allowing multiple parties to jointl... | http://arxiv.org/abs/2311.00314v1 | 2023-11-01T06:00:14 | cs.LG, cs.IR | 2,023 |
Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter | Brisha Jain, Mainack Mondal | Gender bias in political discourse is a significant problem on today's social media. Previous studies found that the gender of politicians indeed influences the content directed towards them by the general public. However, these works are particularly focused on the global north, which represents individualistic cultur... | http://arxiv.org/abs/2310.18911v1 | 2023-10-29T05:41:53 | cs.SI, cs.CY, cs.HC | 2,023 |
Understanding Social Structures from Contemporary Literary Fiction using Character Interaction Graph -- Half Century Chronology of Influential Bengali Writers | Nafis Irtiza Tripto, Mohammed Eunus Ali | Social structures and real-world incidents often influence contemporary literary fiction. Existing research in literary fiction analysis explains these real-world phenomena through the manual critical analysis of stories. Conventional Natural Language Processing (NLP) methodologies, including sentiment analysis, narrat... | http://arxiv.org/abs/2310.16968v1 | 2023-10-25T20:09:14 | cs.CL, cs.CY | 2,023 |
A Roadmap of Emerging Trends Discovery in Hydrology: A Topic Modeling Approach | Sila Ovgu Korkut, Oznur Oztunc Kaymak, Aytug Onan, Erman Ulker, Femin Yalcin | In the new global era, determining trends can play an important role in guiding researchers, scientists, and agencies. The main faced challenge is to track the emerging topics among the stacked publications. Therefore, any study done to propose the trend topics in a field to foresee upcoming subjects is crucial. In the... | http://arxiv.org/abs/2310.15943v1 | 2023-10-24T15:40:05 | cs.CE, E.0; I.7; J.2 | 2,023 |
Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling | Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang | Topic models are one of the compelling methods for discovering latent semantics in a document collection. However, it assumes that a document has sufficient co-occurrence information to be effective. However, in short texts, co-occurrence information is minimal, which results in feature sparsity in document representat... | http://arxiv.org/abs/2310.15420v1 | 2023-10-24T00:23:30 | cs.CL | 2,023 |
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