Identifying User Interests In An Online Discussion Forum With Deep Learning

Topic Model ◽

Model Framework ◽

User Interests ◽

Online Discussion Forum ◽

Average Accuracy ◽

Discussion Threads ◽

Validation Set ◽

Evaluation Metric

The probabilistic topic model Latent Dirichlet Allocation (LDA) was deployed to model the themes of discourse in discussion threads on the social media aggregation website Reddit. Abstracting discussion threads as vectors of topic weights, these vectors were fed into several neural network architectures, each with a different number of hidden layers, to train machine learning models that could identify which discussion would be of interest for a given user to contribute. Using accuracy as the evaluation metric to determine which model framework achieved the best performance on a given user’s validation set, these selected models achieved an average accuracy of 66.1% on the test data for a sample set of 30 users. Using the predicted probabilities of interest made by these neural networks, recommender systems were further built and analyzed for each user.

An Indirected Recommendation Model for Chinese Microblog

Cybernetics and Information Technologies ◽

10.1515/cait-2016-0027 ◽

2016 ◽

Vol 16 (2) ◽

pp. 148-159

Author(s):

Jianyong Duan ◽

Zheng Dong ◽

Mei Zhang

Keyword(s):

Comparative Analysis ◽

Information Sharing ◽

Topic Model ◽

Experimental Results ◽

User Interests ◽

Recommendation Algorithms ◽

Dirichlet Allocation

Abstract Microblog is a browser-based platform for web user’s information sharing and communication. With the rapidly increasing of microblog population, its recommendation function becomes necessary. This paper proposes the recommendation by the Latent Dirichlet Allocation topic model, which combines the user interests into the model to meet their needs. We also conduct a comparative analysis between indirect and direct recommendation algorithms. The experimental results show that the indirect recommendation is more effective for the micro-blog recommendation.

Understanding the University-Sustainability Link through Media: A Spanish Perspective

Sustainability ◽

10.3390/su12124830 ◽

2020 ◽

Vol 12 (12) ◽

pp. 4830 ◽

Cited By ~ 1

Author(s):

Cecilia Elizabeth Bayas Aldaz ◽

Jesus Rodriguez-Pomeda ◽

Leyla Angélica Sandoval Hamón ◽

Fernando Casani

Keyword(s):

Higher Education ◽

Social Perception ◽

Topic Model ◽

Higher Education Institutions ◽

News Coverage ◽

News Sources ◽

University Funding ◽

The Social

This article provides a procedure to universities for understanding the social perception of their activities in the sustainability field, through the analysis of news published in the printed media. It identifies the Spanish news sources that have covered this issue the most and the topics that appear in that news coverage. Using a probabilistic topic model called Latent Dirichlet Allocation, the study includes the nine dominant topics within a corpus with more than seventeen thousand published news items (totaling approximately five and a quarter million words) from a database of almost thirteen hundred national press sources between 2014 and 2017. The study identifies the news sources that published the most news on the issue. It is also found that the amount of news on sustainability and universities declined during the covered period. The nine identified topics point towards the relevance of higher education institutions’ activities as drivers of sustainability. The social perception encapsulated within the topics signals how the public is interested in these activities. Therefore, we find some interesting relationships between sustainable development, higher education institutions’ missions and behaviors, governmental policies, university funding and governance, social and economic innovation, and green campuses in terms of the overall goal of sustainability.

A Text-Mining Analysis on the Review of the Non-Financial Reporting Directive: Bringing Value Creation for Stakeholders into Accounting

Sustainability ◽

10.3390/su13020763 ◽

2021 ◽

Vol 13 (2) ◽

pp. 763

Author(s):

Simona Fiandrino ◽

Alberto Tonelli

Keyword(s):

Text Mining ◽

Financial Reporting ◽

Value Creation ◽

Topic Model ◽

Current Debate ◽

The Core ◽

Integrated Logic

The recent Review of the Non-Financial Reporting Directive (NFRD) aims to enhance adequate non-financial information (NFI) disclosure and improve accountability for stakeholders. This study focuses on this regulatory intervention and has a twofold objective: First, it aims to understand the main underlying issues at stake; second, it suggests areas of possible amendment considering the current debates on sustainability accounting and accounting for stakeholders. In keeping with these aims, the research analyzes the documents annexed to the contribution on the Review of the NFRD by conducting a text-mining analysis with latent Dirichlet allocation (LDA) probabilistic topic model (PTM). Our findings highlight four main topics at the core of the current debate: quality of NFI, standardization, materiality, and assurance. The research suggests ways of improving managerial policies to achieve more comparable, relevant, and reliable information by bringing value creation for stakeholders into accounting. It further addresses an integrated logic of accounting for stakeholders that contributes to sustainable development.

Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach (Preprint)

10.2196/preprints.9222 ◽

2017 ◽

Author(s):

Redhouane Abdellaoui ◽

Pierre Foulquié ◽

Nathalie Texier ◽

Carole Faviez ◽

Anita Burgun ◽

...

Keyword(s):

Social Media ◽

Virtual Communities ◽

Topic Model ◽

Antidepressant Drug ◽

Topic Models ◽

Web Crawler ◽

Manual Review ◽

Model Approach

BACKGROUND Medication nonadherence is a major impediment to the management of many health conditions. A better understanding of the factors underlying noncompliance to treatment may help health professionals to address it. Patients use peer-to-peer virtual communities and social media to share their experiences regarding their treatments and diseases. Using topic models makes it possible to model themes present in a collection of posts, thus to identify cases of noncompliance. OBJECTIVE The aim of this study was to detect messages describing patients’ noncompliant behaviors associated with a drug of interest. Thus, the objective was the clustering of posts featuring a homogeneous vocabulary related to nonadherent attitudes. METHODS We focused on escitalopram and aripiprazole used to treat depression and psychotic conditions, respectively. We implemented a probabilistic topic model to identify the topics that occurred in a corpus of messages mentioning these drugs, posted from 2004 to 2013 on three of the most popular French forums. Data were collected using a Web crawler designed by Kappa Santé as part of the Detec’t project to analyze social media for drug safety. Several topics were related to noncompliance to treatment. RESULTS Starting from a corpus of 3650 posts related to an antidepressant drug (escitalopram) and 2164 posts related to an antipsychotic drug (aripiprazole), the use of latent Dirichlet allocation allowed us to model several themes, including interruptions of treatment and changes in dosage. The topic model approach detected cases of noncompliance behaviors with a recall of 98.5% (272/276) and a precision of 32.6% (272/844). CONCLUSIONS Topic models enabled us to explore patients’ discussions on community websites and to identify posts related with noncompliant behaviors. After a manual review of the messages in the noncompliance topics, we found that noncompliance to treatment was present in 6.17% (276/4469) of the posts.

Phase Segmentation Methods for an Automatic Surgical Workflow Analysis

International Journal of Biomedical Imaging ◽

10.1155/2017/1985796 ◽

2017 ◽

Vol 2017 ◽

pp. 1-17 ◽

Cited By ~ 7

Author(s):

Dinh Tuan Tran ◽

Ryuhei Sakurai ◽

Hirotake Yamazoe ◽

Joo-Ho Lee

Keyword(s):

Topic Model ◽

Promising Result ◽

Average Length ◽

Surgical Workflow ◽

Surgical Workflow Analysis ◽

Average Accuracy ◽

Flow Motion ◽

Motion Features ◽

Leave One Out

In this paper, we present robust methods for automatically segmenting phases in a specified surgical workflow by using latent Dirichlet allocation (LDA) and hidden Markov model (HMM) approaches. More specifically, our goal is to output an appropriate phase label for each given time point of a surgical workflow in an operating room. The fundamental idea behind our work lies in constructing an HMM based on observed values obtained via an LDA topic model covering optical flow motion features of general working contexts, including medical staff, equipment, and materials. We have an awareness of such working contexts by using multiple synchronized cameras to capture the surgical workflow. Further, we validate the robustness of our methods by conducting experiments involving up to 12 phases of surgical workflows with the average length of each surgical workflow being 12.8 minutes. The maximum average accuracy achieved after applying leave-one-out cross-validation was 84.4%, which we found to be a very promising result.

Integrating Topic, Sentiment, and Syntax for Modeling Online Reviews: A Topic Model Approach

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4041475 ◽

2018 ◽

Vol 19 (1) ◽

Cited By ~ 4

Author(s):

Min Tang ◽

Jian Jin ◽

Ying Liu ◽

Chunping Li ◽

Weiwen Zhang

Keyword(s):

Visual Information ◽

Topic Model ◽

Online Reviews ◽

Sentiment Classification ◽

Academic Field ◽

Aspect Extraction ◽

Part Of Speech ◽

Topic Sentiment

Analyzing product online reviews has drawn much interest in the academic field. In this research, a new probabilistic topic model, called tag sentiment aspect models (TSA), is proposed on the basis of Latent Dirichlet allocation (LDA), which aims to reveal latent aspects and corresponding sentiment in a review simultaneously. Unlike other topic models which consider words in online reviews only, syntax tags are taken as visual information and, in this research, as a kind of widely used syntax information, part-of-speech (POS) tags are first reckoned. Specifically, POS tags are integrated into three versions of implementation in consideration of the fact that words with different POS tags might be utilized to express consumers' opinions. Also, the proposed TSA is one unsupervised approach and only a small number of positive and negative words are required to confine different priors for training. Finally, two big datasets regarding digital SLR and laptop are utilized to evaluate the performance of the proposed model in terms of sentiment classification and aspect extraction. Comparative experiments show that the new model can not only achieve promising results on sentiment classification but also leverage the performance on aspect extraction.

Evaluating individual genome similarity with a topic model

Bioinformatics ◽

10.1093/bioinformatics/btaa583 ◽

2020 ◽

Vol 36 (18) ◽

pp. 4757-4764

Author(s):

Liran Juan ◽

Yongtian Wang ◽

Jingyi Jiang ◽

Qi Yang ◽

Guohua Wang ◽

...

Keyword(s):

Rare Variants ◽

Topic Model ◽

Principal Component ◽

Data Access ◽

Supplementary Information ◽

Sequencing Technology ◽

Individual Genome ◽

Individual Level ◽

Probabilistic Topic Model

Abstract Motivation Evaluating genome similarity among individuals is an essential step in data analysis. Advanced sequencing technology detects more and rarer variants for massive individual genomes, thus enabling individual-level genome similarity evaluation. However, the current methodologies, such as the principal component analysis (PCA), lack the capability to fully leverage rare variants and are also difficult to interpret in terms of population genetics. Results Here, we introduce a probabilistic topic model, latent Dirichlet allocation, to evaluate individual genome similarity. A total of 2535 individuals from the 1000 Genomes Project (KGP) were used to demonstrate our method. Various aspects of variant choice and model parameter selection were studied. We found that relatively rare (0.001<allele frequency < 0.175) and sparse (average interval > 20 000 bp) variants are more efficient for genome similarity evaluation. At least 100 000 such variants are necessary. In our results, the populations show significantly less mixed and more cohesive visualization than the PCA results. The global similarities among the KGP genomes are consistent with known geographical, historical and cultural factors. Availability and implementation The source code and data access are available at: https://github.com/lrjuan/LDA_genome. Supplementary information Supplementary data are available at Bioinformatics online.

SPUCL (Scientific Publication Classifier): A Human-Readable Labelling System for Scientific Publications

Applied Sciences ◽

10.3390/app11199154 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9154

Author(s):

Noemi Scarpato ◽

Alessandra Pieroni ◽

Michela Montorsi

Keyword(s):

Scientific Literature ◽

Topic Model ◽

Scientific Publication ◽

Research Field ◽

Classifier Systems ◽

Scientific Publications ◽

Probabilistic Machine Learning ◽

Labelling System

To assess critically the scientific literature is a very challenging task; in general it requires analysing a lot of documents to define the state-of-the-art of a research field and classifying them. The documents classifier systems have tried to address this problem by different techniques such as probabilistic, machine learning and neural networks models. One of the most popular document classification approaches is the LDA (Latent Dirichlet Allocation), a probabilistic topic model. One of the main issues of the LDA approach is that the retrieved topics are a collection of terms with their probabilities and it does not have a human-readable form. This paper defines an approach to make LDA topics comprehensible for humans by the exploitation of the Word2Vec approach.

SDTM: A Novel Topic Model Framework for Syndrome Differentiation in Traditional Chinese Medicine

Journal of Healthcare Engineering ◽

10.1155/2022/6938506 ◽

2022 ◽

Vol 2022 ◽

pp. 1-12

Author(s):

Jialin Ma ◽

Xiaoqiang Gong ◽

Zhaojun Wang ◽

Qian Xie

Keyword(s):

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Medical Records ◽

Bayesian Method ◽

Topic Model ◽

Syndrome Differentiation ◽

Semantic Relationship ◽

Model Framework ◽

Intelligent Methods

Syndrome differentiation is the most basic diagnostic method in traditional Chinese medicine (TCM). The process of syndrome differentiation is difficult and challenging due to its complexity, diversity, and vagueness. Recently, artificial intelligent methods have been introduced to discover the regularities of syndrome differentiation from TCM medical records, but the existing DM algorithms failed to consider how a syndrome is generated according to TCM theories. In this paper, we propose a novel topic model framework named syndrome differentiation topic model (SDTM) to dynamically characterize the process of syndrome differentiation. The SDTM framework utilizes latent Dirichlet allocation (LDA) to discover the latent semantic relationship between symptoms and syndromes in mass of Chinese medical records. We also use similarity measurement method to make the uninterpretable topics correspond with the labeled syndromes. Finally, Bayesian method is used in the final differentiated syndromes. Experimental results show the superiority of SDTM over existing topic models for the task of syndrome differentiation.