Yang Liu, Minghui Qiu, Swapna Gottipati et al. · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · 2013

Yang, L., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., & Chen, Z. (2013). Topic Expertise Model. Available at Github - https://github.com/minghui/TopicExpertiseModel

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Exclusive Topic Model

Lei Hao, Kailiang Liu, Ying Chen · 2023

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Topic interpretation for the 5-topic model.

Annisa Marlin Masbar Rus (22272323), Julie S. Ivy (17061137), Min Chi (6832679) et al. · Figshare · 2025

<div> Diabetic Retinopathy (DR) is a complication related to diabetes that can lead to vision impairment. To assist DR patients, a care management company provides a telephone-based principal care management (PCM) service, which includes care coaching and other services to reduce barriers to care for patients with DR. Despite its benefits, enrollment in the program is suboptimal. This study developed predictive models using call transcripts to investigate factors associated with patient enrollment in the PCM service. We analyzed transcripts of calls made during the enrollment process (prior to enrollment) and feature-engineered the call metadata (i.e., transcript length, number of calls, time between calls, customer and agent sentiment). In addition, we extracted topics discussed in the transcripts using Structural Topic Modeling (STM) and converted them into vector representations. Utilizing call metadata alongside topics, we developed three classification models (call metadata, topic-based, and topic+metadata) to predict patient enrollment, with the latter demonstrating superior performance. The topic+metadata classification model outperformed the other two models in distinguishing between patient enrollment and non-enrollment, with AUC values ranging from 0.81 to 0.99 across models using 3 to 15-topics. The findings suggest that proactively offering to schedule an appointment after the program benefits explanation leads to a higher odds of enrollment. When the scheduling portion of the conversation is not considered, agents should cover all parts of the script over multiple calls. Additionally, agents who explain the program and maintain longer intervals between calls have higher odds of patient enrollment, suggesting that there is value in allowing patients adequate time to reflect between calls. These findings offer valuable insights for agents to evaluate their strategies in patient enrollment. As the first point of contact, enrollment agents play a crucial role in determining whether patients can benefit from care coordination and management programs. </div>

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Topic Evolution Analysis Based on Cluster Topic Model

Yaoyi Xi, Gang Chen, Bicheng Li et al. · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2016

Topic evolution analysis helps to understand how the topics evolve or develop along the timeline. Aiming at the problem that existing researches did not mine the latent semantic information in depth and needed to pre-determine the number of clusters, this paper proposes cluster topic model based method to analyze topic evolution analysis. Firstly, a new topic model, namely cluster topic model, is built to complete document clustering while mining latent semantic information. Secondly, events are detected according to the cluster label of each document and evolution relationship between any two events is identified based on the aspect distributions of documents. Finally, by choosing the representative document of each event, topic evolution graph is constructed to display the development of the topic along the timeline. Experiments are presented to show the performance of our proposed technique. It is found that our proposed technique outperforms the comparable techniques in previous work.

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Personalized Topic Selection Model for Topic-Grounded Dialogue

Shixuan Fan, Wei Wei, Xiaofei Wen et al. · 2024

Recently, the topic-grounded dialogue (TGD) system has become increasingly popular as its powerful capability to actively guide users to accomplish specific tasks through topic-guided conversations.Most existing works utilize side information (e.g., topics or personas) in isolation to enhance the topic selection ability.However, due to disregarding the noise within these auxiliary information sources and their mutual influence, current models tend to predict useruninteresting and contextually irrelevant topics.To build user-engaging and coherent dialogue agent, we propose a Personalized topic sElection model for Topic-grounded Dialogue, named PETD, which takes account of the interaction of side information to selectively aggregate such information for more accurately predicting subsequent topics.Specifically, we evaluate the correlation between global topics and personas and selectively incorporate the global topics aligned with user personas.Furthermore, we propose a contrastive learning based persona selector to filter out irrelevant personas under the constraint of lacking pertinent persona annotations.Throughout the selection and generation, diverse relevant side information is considered.Extensive experiments demonstrate that our proposed method can generate engaging and diverse responses, outperforming state-of-the-art baselines across various evaluation metrics.

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Latent interest-topic model

Noriaki Kawamae · 2010

This paper presents a hierarchical generative model that captures the latent relation of cause and effect underlying user behavioral-originated data such as papers, twitter and purchase history. Our proposel, the Latent Interest Topic model (LIT), introduces a latent variable into each document and each author layor in a coherent generative model. We call the former variable the document class, and the latter variable the author class, where these classes are indicator variables that allow the inclusion of different types of probability, and can be shared over documents with similar content and authors with similar interests, respectively. Significantly, unlike other works, LIT differentiates, respectively, document topics and user interests by using these classes. Consequently, LIT is superior to previous models in explaining the causal relationships behind the data by merging similar distributions; it also makes the computation process easier. Experiments on a research paper corpus show that the proposed model can well capture document and author classes, and reduce the dimensionality of documents to a low-dimensional author-document space, making it useful as a generative model.

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User-sentiment topic model

Tong Zhao, Chunping Li, Qiang Ding et al. · 2012

In large social networks, users feel free to share their feelings about anything they are interested in and many research works have focused on modeling users' interests on social network for product recommendations or personal services. Unfortunately, there are fewer works about finding why users like or dislike something. More specifically, there are many researches about sentiment analysis of users' opinion toward products or topics, but fewer are focused on why they hold this feeling and which aspects or factors of the product (topic) lead to users' different opinions about it. In this paper, we present a hierarchical generative model, called user-sentiment topic model (USTM), which captures users' topics with sentiment information. Our aim is to use USTM to refine users' topics with different sentiment trends including positive, negative and neutral, which can be further used in social network analysis to find influential users on topic level with sentiment information. The experiment results on three datasets show that our proposed USTM can capture user's interests with their sentiment well, making it useful for social network analysis.

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Multilingual Dynamic Topic Model

Elaine Zosa, Mark Granroth-Wilding · 2019

Dynamic topic models (DTMs) capture the evolution of topics and trends in time series data. Current DTMs are applicable only to monolingual datasets. In this paper we present the multilingual dynamic topic model (ML-DTM), a novel topic model that combines DTM with an existing multilingual topic modeling method to capture crosslingual topics that evolve across time. We present results of this model on a parallel German-English corpus of news articles and a comparable corpus of Finnish and Swedish news articles. We demonstrate the capability of ML-DTM to track significant events related to a topic and show that it finds distinct topics and performs as well as existing multilingual topic models in aligning cross-lingual topics.

Cited by 15Open access

Recurrent Embedded Topic Model

Carlos Vargas, Hiram Pönce · Applied Sciences · 2023

In this paper we propose the Recurrent Embedded Topic Model (RETM) which is a modification of the Embedded Topic Modelling (ETM) by reusing the Continuous Bag of Words (CBOW) that the model had implemented and applying it to a recurrent neural network (LSTM), using the order of the words of the text, in the CBOW space as the recurrency of the LSTM, while calculating the topic–document distribution of the model. This approach is novel because the ETM and Latent Dirichlet Allocation (LDA) do not use the order of the words while calculating the topic proportions for each text, making worse predictions in the end. The RETM is a topic-modelling technique that vastly improves (by more than 15 times in train data and between 10% and 90% better based on test dataset values for perplexity) the quality of the topics that were calculated for the datasets used in this paper. This model is explained in detail throughout the paper and presents results on different use cases on how the model performs against ETM and LDA. The RETM can be used with better accuracy for any topic model-related problem.

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Graph Contrastive Topic Model

Zheheng Luo, Lei Liu, Qianqian Xie et al. · SSRN Electronic Journal · 2024

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Topic model of cerme

Jonas Dreyøe Herfort, Andreas Lindenskov Tamborg, Florian Meier et al. · VBN Forskningsportal (Aalborg Universitet) · 2022

Technical appendix for ESM paper. The list of topic distribution. The topic model visualization. The code that produced the model, for full transparency.

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Geographic Topic Model: Appendix

Jacob Eisenstein · 2012

Faceted topic models combine topical content with extraneous facets, such as ideology or dialect. In this model, the “pure ” topics are corrupted by the facets, using a hierarchical generative model in which the pure topics act as priors on the faceted topics. This is most easily modeled using the logistic-normal distribution, which admits a normal prior on the mean.

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Activity-Aware Topic Model

2016

In everyday life, we keep receiving recommendations from others either by words of mouth, press print, or multi-media such as TV advertising. Nowadays, recommender systems are entering our life online: advertising about something you searched the other day in Google appears in the right side of your current searching page; Amazon always tries to guess what you would be interested in and give some similar items as recommendations; content sites such as StumbleUpon provide information in particular areas which you set before. This research will serve for recommender systems by focusing on capturing people’s activities from tweets to find the associated preference topics. Such recommender system is supposed to provide more targeted information with accompanied comprehensive recommendations. The research priority is to build a categorized database for the reference of activity-aware topics. The tri-layer clusters are expected, which consist of a general topic layer, a detailed activity layer and a word layer. Fig. 1 gives two specific examples of the expected tri-layer clusters. The data source is twitter posts, and we propose two topic models to generate the tri-layer clusters.

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People Opinion Topic Model

Hongxu Chen, Hongzhi Yin, Xue Li et al. · 2017

Mining various hot discussed topics and corresponding opinions from different groups of people in social media (e.g., Twitter) is very useful. For example, a decision maker in a company wants to know how different groups of people (customers, staff, competitors, etc.) think about their services, facilities, and things happened around. In this paper, we are focusing on the problem of finding opinion variations based on different groups of people and introducing the concept of opinion based community detection. Further, we also introduce a generative graphic model, namely People Opinion Topic (POT) model, which detects social communities, associated hot discussed topics, and perform sentiment analysis simultaneously by modelling user's social connections, common interests, and opinions in a unified way. This paper is the first attempt to study community and opinion mining together. Compared with traditional social communities detection, the detected communities by POT model are more interpretable and meaningful. In addition, we further analyse how diverse opinions distributed and propagated among various social communities. Experiments on real twitter dataset indicate our model is effective.

Cited by 31Open access