scholarly journals Topic Modeling of Committee Discussions in the Brazilian Chamber of Deputies

2021 ◽  
Author(s):  
M. A. dos Santos ◽  
N. Andrade ◽  
F. Morais
Keyword(s):  
Language Processing ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Structured Data ◽  
National Congress ◽  
Similarities And Differences ◽  
External Events ◽  
Latent Topics ◽  
Processing Techniques ◽  
Chamber Of Deputies

Ensuring that civil society can monitor and supervise the actions of its representatives is essential to build strong democracies. Despite significant advances in transparency, Brazilian National Congress committees are presently complex to follow and monitor due to the lack of open structured data about their discussions and the sheer volume of activity in these committees. This work presents two contributions to this context. First, we create and present an open dataset including structured speeches of the 25 Chamber of Deputies' standing committees over the last two decades. Second, we use Natural Language Processing techniques - especially Latent Dirichlet Allocation (LDA) - to identify themes addressed on these committees. Based on these latent topics, we explore similarities and differences between the standing committees, their relationships, and how their debates change over time. Our results show that committees accommodate conversations - including their main topic and opposing agendas - and describe how the topics discussed in the committees reverberate external events.

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Disaster Response ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Situational Awareness ◽  
High Performing ◽  
Latent Topics ◽  
Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

Analysis and Visualization Latent Topic on COVID-19 Vaccine Tweet use two-stage topic modeling (Preprint)

2021 ◽  
Author(s):  
Faizah Faizah ◽  
Bor-Shen Lin
Keyword(s):  
Topic Modeling ◽  
Public Perception ◽  
Latent Dirichlet Allocation ◽  
World Health ◽  
Two Stage ◽  
The Public ◽  
Global Pandemic ◽  
Difficult Time ◽  
Latent Topic ◽  
Latent Topics

BACKGROUND The World Health Organization (WHO) declared COVID-19 as a global pandemic on January 30, 2020. However, the pandemic has not been over yet. Furthermore, in the first quartal of 2021, some countries face the third wave of the pandemic. During the difficult time, the development of the vaccines for COVID-19 accelerates rapidly. Understanding the public perception of the COVID-19 Vaccine according to the data collected from social media can widen the perspective on the state of the global pandemic OBJECTIVE This study explores and analyzes the latent topic on COVID-19 Vaccine Tweet posted by individuals from various countries by using two-stage topic modeling. METHODS A two-stage analysis in topic modeling was proposed to investigating people’s reactions in five countries. The first stage is Latent Dirichlet Allocation that produces the latent topics with the corresponding term distributions that facilitate the investigators to understand the main issues or opinions. The second stage then performs agglomerative clustering on the latent topics based on Hellinger distance, which merges close topics hierarchically into topic clusters to visualize those topics in either tree or graph views. RESULTS In general, the topic discussion regarding the COVID-19 Vaccine in five countries is similar. Topic themes such as "first vaccine" and & "vaccine effect" dominate the public discussion. The remarkable point is that people in some countries have some topic themes, such as "politician opinion" and " stay home" in Canada, "emergency" in India, and & "blood clots" in the United Kingdom. The analysis also shows the most popular COVID-19 Vaccine, which is gaining more public interest. CONCLUSIONS With LDA and Hierarchical clustering, two-stage topic modeling is powerful for visualizing the latent topics and understanding the public perception regarding the COVID-19 Vaccine.

scholarly journals Authorship verification

2019 ◽  
Author(s):  
Νεκταρία Πόθα
Keyword(s):  
Cyber Security ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Latent Semantic Indexing ◽  
Semantic Indexing ◽  
Authorship Verification ◽  
Latent Topics ◽  
Authorship Analysis ◽  
Dirichlet Allocation

Η περιοχή της ανάλυσης συγγραφέα (Authorship Analysis) αποσκοπεί στην άντληση πληροφοριών σχετικά με τους συγγραφείς ψηφιακών κειμένων. Συνδέεται άμεσα με πολλές εφαρμογές καθώς είναι εφικτό να χρησιμοποιηθεί για την ανάλυση οποιουδήποτε είδους(genre) κειμένων: λογοτεχνικών έργων, άρθρων εφημερίδων, αναρτήσεις σε κοινωνικά δίκτυα κλπ. Οι περιοχές εφαρμογών της τεχνολογίας αυτής διακρίνονται σε φιλολογικές (humanities),(π.χ. ποιος είναι ο συγγραφέας ενός λογοτεχνικού έργου που εκδόθηκε ανώνυμα, ποιος είναι ο συγγραφέας έργων που έχουν εκδοθεί με ψευδώνυμο, επαλήθευση της πατρότητας λογοτεχνικών έργων γνωστών συγγραφέων κτλ.), εγκληματολογικές (forensics) (π.χ. εύρεση υφολογικών ομοιοτήτων μεταξύ προκηρύξεων τρομοκρατικών ομάδων, διερεύνηση αυθεντικότητας σημειώματος αυτοκτονίας, αποκάλυψη πολλαπλών λογαριασμών χρήστη σε κοινωνικά δίκτυα που αντιστοιχούν στο ίδιο άτομο κτλ.) και στον τομέα της ασφάλειας του κυβερνοχώρου (cyber-security) (π.χ. εύρεση υφολογικών ομοιοτήτων μεταξύ χρηστών πολλαπλών ψευδωνύμων).Θεμελιώδες ερευνητικό πεδίο της ανάλυσης συγγραφέα αποτελεί η επαλήθευση συγγραφέα (author verification), όπου δεδομένου ενός συνόλου κειμένων (σε ηλεκτρονική μορφή) από τον ίδιο συγγραφέα (υποψήφιος συγγραφέας) καλούμαστε να αποφασίσουμε αν ένα άλλο κείμενο (άγνωστης ή αμφισβητούμενης συγγραφικής προέλευσης) έχει γραφτεί από τον συγγραφέα αυτόν ή όχι. Η επαλήθευση συγγραφέα έχει αποκτήσει ιδιαίτερο ενδιαφέρον τα τελευταία χρόνια κυρίως λόγω των πειραματικών αξιολογήσεων PAN@CLEF. Συγκεκριμένα, από το 2013 εως το 2015 οι διαγωνισμοί PAN είχαν εστιάσει στο πεδίο της επαλήθευσης συγγραφέα παρέχοντας ένα καλά οργανωμένο σύνολο δεδομένων (PAN corpora) και συγκεντρώνοντας πλήθος μεθόδων για τον σκοπό αυτό. Ωστόσο, το περιθώριο λάθους είναι αρκετά μεγάλο εφόσον η επίδοση των μεθόδων εξαρτάται από πολλαπλούς παράγοντες όπως το μήκος των κειμένων, η θεματική συνάφεια μεταξύ των κειμένων και η υφολογική συνάφεια μεταξύ των κειμένων. Η πιο απαιτητική περίπτωση προκύπτει όταν τα κείμενα γνωστού συγγραφέα ανήκουν σε ένα είδος (π.χ. blogs ή μηνύματα email) ενώ το προς διερεύνηση κείμενο ανήκει σε άλλο είδος (π.χ., tweet ή άρθρο εφημερίδας). Επιπλέον, αν τα κείμενα του γνωστού συγγραφέα με το προς διερεύνηση κείμενο δεν συμφωνούν ως προς τη θεματική περιοχή (topic) (π.χ. τα γνωστά κείμενα σχετίζονται με εξωτερική πολιτική και το άγνωστο με πολιτιστικά θέματα) η επίδοση των τρεχόντων μεθόδων επαλήθευσης συγγραφέα είναι ιδιαίτερα χαμηλή. Στόχος της παρούσας διδακτορικής διατριβής είναι η ανάπτυξη αποδοτικών και εύρωστων μεθόδων επαλήθευσης συγγραφέα που είναι ικανές να χειριστούν ακόμα και τέτοιες περίπλοκες περιπτώσεις. Προς την κατεύθυνση αυτή, παρουσιάζουμε βελτιωμένες μεθόδους επαλήθευσης συγγραφέα και συστηματικά εξετάζουμε την αποδοτικότητα τους σε διάφορα σύνολα δεδομένων αναφοράς (PAN datasets και Enron Data). Αρχικά, προτείνουμε δύο βελτιωμένους αλγόριθμους, ο ένας ακολουθεί το παράδειγμα όπου όλα τα διαθέσιμα δείγματα γραφής του υποψηφίου συγγραφέα αντιμετωπίζονται μεμονωμένα, ως ξεχωριστές αναπαραστάσεις (instance-based paradigm) και ο άλλος είναι βασισμένος στο παράδειγμα όπου όλα τα δείγματα γραφής του υποψηφίου συγγραφέα συννενώνονται και εξάγεται ένα ενιαίο κείμενο, μία μοναδική αναπαράσταση (profile-based paradigm), οι οποίες επιτυγχανουν υψηλότερη απόδοση σε σύνολα δεδομένων που καλύπτουν ποικιλία γλωσσώνν (Αγγλικά, Ελληνικά, Ισπανικά, Ολλανδικά) και κειμενικών ειδών (άρθρα, κριτικές, νουβέλες, κ.ά.) σε σύγκριση με την τεχνολογία αιχμής (state-of-the-art) στον τομέα της επαλήθευσης. Είναι σημαντικό να τονίσουμε ότι οι προτεινόμενες μέθοδοι επωφελούνται σημαντικά από τη διαθεσιμότητα πολλαπλών δειγμάτων κειμένων του υποψηφίου συγγραφέα και παραμένουν ιδιαίτερα ανθεκτικές/ανταγωνιστικές όταν το μήκος των κειμένων είναι περιορισμένο. Επιπλέον, διερευνούμε τη χρησιμότητα της εφαρμογής μοντελοποίησης θέματος (topic modeling) στην επαλήθευση συγγραφέα. Συγκεκριμένα, διεξάγουμε μια συστηματική μελέτη για να εξετάσουμε εάν οι τεχνικές μοντελοποίησης θέματος επιτυγχάνουν την βελτίωση της απόδοσης των πιο βασικών κατηγοριών μεθόδων επαλήθευσης καθώς και ποια συγκεκριμένη τεχνική μοντελοποίησης θέματος είναι η πλέον κατάλληλη για κάθε ένα από τα παραδείγματα μεθόδων επαλήθευσης. Για το σκοπό αυτό, συνδυάζουμε γνωστές μεθόδους μοντελοποίσης, Latent Semantic Indexing (LSI) και Latent Dirichlet Allocation, (LDA), με διάφορες μεθόδους επαλήθευσης συγγραφέα, οι οποίες καλύπτουν τις βασικές κατηγορίες στην περιοχή αυτή, δηλαδή την ενδογενή(intrinsic), που αντιμετωπίζει το πρόβλημα επαλήθευσης ως πρόβλημα μίας κλάσης, και την εξωγενή (extrinsic), που μετατρέπει το πρόβλημα επαλήθευσης σε πρόβλημα δύο κλάσεων, σε συνδυασμό με τις profile-based και instance-based προσεγγίσεις.Χρησιμοποιώντας πολλαπλά σύνολα δεδομένων αξιολόγησης επιδεικνύουμε ότι η LDA τεχνική συνδυάζεται καλύτερα με τις εξωγενείς μεθόδους ενώ η τεχνική LSI αποδίδει καλύτερα με την πιο αποδοτικής ενδογενή μέθοδο. Επιπλέον, οι τεχνικές μοντελοποίησης θέματος φαίνεται να είναι πιο αποτελεσματικές όταν εφαρμόζονται σε μεθόδους που ακολουθούν το profile-based παράδειγμα και η αποδοτικότητα τους ενισχύεται όταν η πληροφορία των latent topics εξάγεται από ένα ενισχυμένο σύνολο κειμένων (εμπλουτισμένο με επιπλέον κείμενα τα οποία έχουν συλλεχθεί από εξωτερικές πηγές (π.χ web) και παρουσιάζουν σημαντική θεματική συνάφεια με το αρχικό υπό εξέταση σύνολο δεδομένων. Η σύγκριση των αποτελεσμάτων μας με την τεχνολογία αιχμής του τομέα της επαλήθευσης, επιδεικνύει την δυναμική των προτεινόμενων μεθόδων. Επίσης, οι προτεινόμενες εξωγενείς μέθοδοι είναι ιδιαίτερα ανταγωνιστικές στην περίπτωση που χρησιμοποιηθούν αγνώστου είδους εξωγενή κείμενα. Σε ορισμένες από τις σχετικές μελέτες, υπάρχουν ενδείξεις ότι ετερογενή σύνολα(heterogeneous ensembles) μεθόδων επαλήθευσης μπορούν να παρέχουν πολύ αξιόπιστες λύσεις, καλύτερες από κάθε ατομικό μοντέλο επαλήθευσης ξεχωριστά. Ωστόσο, έχουν εξεταστεί μόνο πολύ απλά μοντέλα συνόλων έως τώρα που συνδυάζουν σχετικά λίγες βασικές μεθόδους. Προσπαθώντας να καλύψουμε το κενό αυτό, θεωρούμε ένα μεγάλο σύνολο βασικών μοντέλων επαλήθευσης (συνολικά 47 μοντέλα) που καλύπτουν τα κύρια παραδείγματα /κατηγορίες μεθόδων στην περιοχή αυτή και μελετούμε τον τρόπο με τον οποίο μπορούν να συνδυαστούν ώστε να δημιουργηθεί ένα αποτελεσματικό σύνολο. Με αυτό τον τρόπο, προτείνουμε ένα απλό σύνολο ομαδοποίησης στοίβας (stacking ensemble) καθώς και μια προσέγγιση που βασίζεται στην δυναμική επιλογή μοντέλων για καθεμία υπό εξέταση περίπτωση επαλήθευσης συγγραφέα ξεχωριστά. Τα πειραματικά αποτελέσματα σε πολλαπλά σύνολα δεδομένων επιβεβαιώνουν την καταλληλότητα των προτεινόμενων μεθόδων επιδεικνύοντας την αποτελεσματικότητα τους. Η βελτίωση της επίδοσης που επιτυγχάνουν τα καλύτερα από τα αναφερόμενα μοντέλα σε σχέση με την τρέχουσα τεχνολογία αιχμής είναι περισσότερο από 10%.

scholarly journals Topic Modeling for Keyword Extraction: using Natural Language Processing methods for keyword extraction in Portal Min@s

2015 ◽  
Vol 23 (3) ◽  
pp. 695 ◽  
Author(s):  
Arnaldo Candido Junior ◽  
Célia Magalhães ◽  
Helena Caseli ◽  
Régis Zangirolami
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Keyword Extraction ◽  
Processing Methods ◽  
Dirichlet Allocation

<p style="margin-bottom: 0cm; line-height: 100%;" align="justify"> </p><p>Este artigo tem o objetivo da avaliar a aplicação de dois métodos automáticos eficientes na extração de palavras-chave, usados pelas comunidades da Linguística de <em>Corpus </em>e do Processamento da Língua Natural para gerar palavras-chave de textos literários: o <em>WordSmith Tools </em>e o <em>Latent Dirichlet Allocation </em>(LDA). As duas ferramentas escolhidas para este trabalho têm suas especificidades e técnicas diferentes de extração, o que nos levou a uma análise orientada para a sua performance. Objetivamos entender, então, como cada método funciona e avaliar sua aplicação em textos literários. Para esse fim, usamos análise humana, com conhecimento do campo dos textos usados. O método LDA foi usado para extrair palavras-chave por meio de sua integração com o <em>Portal Min@s: Corpora de Fala e Escrita</em>, um sistema geral de processamento de <em>corpora</em>, concebido para diferentes pesquisas de Linguística de <em>Corpus</em>. Os resultados do experimento confirmam a eficácia do WordSmith Tools e do LDA na extração de palavras-chave de um <em>corpus </em>literário, além de apontar que é necessária a análise humana das listas em um estágio anterior aos experimentos para complementar a lista gerada automaticamente, cruzando os resultados do WordSmith Tools e do LDA. Também indicam que a intuição linguística do analista humano sobre as listas geradas separadamente pelos dois métodos usados neste estudo foi mais favorável ao uso da lista de palavras-chave do WordSmith Tools.</p>

scholarly journals A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

2019 ◽  
Vol 12 (1) ◽  
pp. 320 ◽  
Author(s):  
Wafa Shafqat ◽  
Yung-Cheol Byun
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Recommendation Systems ◽  
Support Vector ◽  
Latent Factors ◽  
Internet Applications ◽  
Enormous Amount ◽  
Combined Features ◽  
Latent Topics ◽  
User History

With rapid advancements in internet applications, the growth rate of recommendation systems for tourists has skyrocketed. This has generated an enormous amount of travel-based data in the form of reviews, blogs, and ratings. However, most recommendation systems only recommend the top-rated places. Along with the top-ranked places, we aim to discover places that are often ignored by tourists owing to lack of promotion or effective advertising, referred to as under-emphasized locations. In this study, we use all relevant data, such as travel blogs, ratings, and reviews, in order to obtain optimal recommendations. We also aim to discover the latent factors that need to be addressed, such as food, cleanliness, and opening hours, and recommend a tourist place based on user history data. In this study, we propose a cross mapping table approach based on the location’s popularity, ratings, latent topics, and sentiments. An objective function for recommendation optimization is formulated based on these mappings. The baseline algorithms are latent Dirichlet allocation (LDA) and support vector machine (SVM). Our results show that the combined features of LDA, SVM, ratings, and cross mappings are conducive to enhanced performance. The main motivation of this study was to help tourist industries to direct more attention towards designing effective promotional activities for under-emphasized locations.

scholarly journals Latent Dirichlet Allocation in predicting clinical trial terminations

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Simon Geletta ◽  
Lendie Follett ◽  
Marcia Laugerman
Keyword(s):  
Clinical Trial ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Structured Data ◽  
Unstructured Data ◽  
Future Research ◽  
Funding Agencies ◽  
Dirichlet Allocation

Abstract Background This study used natural language processing (NLP) and machine learning (ML) techniques to identify reliable patterns from within research narrative documents to distinguish studies that complete successfully, from the ones that terminate. Recent research findings have reported that at least 10 % of all studies that are funded by major research funding agencies terminate without yielding useful results. Since it is well-known that scientific studies that receive funding from major funding agencies are carefully planned, and rigorously vetted through the peer-review process, it was somewhat daunting to us that study-terminations are this prevalent. Moreover, our review of the literature about study terminations suggested that the reasons for study terminations are not well understood. We therefore aimed to address that knowledge gap, by seeking to identify the factors that contribute to study failures. Method We used data from the clinicialTrials.gov repository, from which we extracted both structured data (study characteristics), and unstructured data (the narrative description of the studies). We applied natural language processing techniques to the unstructured data to quantify the risk of termination by identifying distinctive topics that are more frequently associated with trials that are terminated and trials that are completed. We used the Latent Dirichlet Allocation (LDA) technique to derive 25 “topics” with corresponding sets of probabilities, which we then used to predict study-termination by utilizing random forest modeling. We fit two distinct models – one using only structured data as predictors and another model with both structured data and the 25 text topics derived from the unstructured data. Results In this paper, we demonstrate the interpretive and predictive value of LDA as it relates to predicting clinical trial failure. The results also demonstrate that the combined modeling approach yields robust predictive probabilities in terms of both sensitivity and specificity, relative to a model that utilizes the structured data alone. Conclusions Our study demonstrated that the use of topic modeling using LDA significantly raises the utility of unstructured data in better predicating the completion vs. termination of studies. This study sets the direction for future research to evaluate the viability of the designs of health studies.

A Technique to Exploit Free-Form Notes to Predict Customer Churn

2014 ◽  
Vol 4 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Gregory W. Ramsey ◽  
Sanjay Bapna
Keyword(s):  
Language Processing ◽  
Resource Planning ◽  
Classification Problem ◽  
Structured Data ◽  
Free Form ◽  
Care Providers ◽  
Health Records ◽  
Hospital Administrators ◽  
Customer Churn ◽  
Processing Techniques

As healthcare costs rise, hospitals are seeking ways to improve operations. This paper examines the usefulness of free-form notes to solve a classification problem commonly associated with customer churn. The authors show that classifiers which incorporate free-form notes, using natural language processing techniques, are up to 9% more accurate than classifiers that are solely developed using structured data. The authors suggest that hospitals and chronic disease management clinics can use structured data and free-form notes from electronic health records to predict which patients are likely to cease receiving care from their facilities. Classification tools for predicting patient churn are of interest to hospital administrators; such information can aid in resource planning and facilitate smoother handoffs between care providers.

Hidden Stories in Hydrologic Literature: An Interactive Topic-Based Ontology

2020 ◽  
Author(s):  
Mashrekur Rahman ◽  
Grey Nearing ◽  
Jonathan Frame
Keyword(s):  
Water Resources ◽  
Computational Linguistics ◽  
Language Processing ◽  
Topic Modeling ◽  
Science Communication ◽  
Latent Dirichlet Allocation ◽  
Relevant Literature ◽  
Optimal Number ◽  
Primary Objective ◽  
Climate Change Research

&lt;p&gt;Hydrologic research generates massive volumes of peer-reviewed literature across a plethora of evolving topics and sub-topics. It&amp;#8217;s becoming increasingly difficult for scientists and practitioners to synthesize and leverage the full body of scientific literature. Recent advancement of computational linguistics, machine learning, including a variety of toolboxes for Natural Language Processing (NLP), help facilitate analysis of vast electronic corpuses for a multitude of objectives. Research papers published as electronic text files in different journals offer windows into trending topics and developments, and NLP allows us to extract information and insight about these trends.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;This project applies Latent Dirichlet Allocation (LDA) Topic Modeling for bibliometric analyses of all peer-reviewed articles in selected high-impact (Impact Factor &gt; 0.9) journals in hydrology (&lt;em&gt;Water Resources Research, Hydrology and Earth System Sciences, Journal of Hydrology,&amp;#160; Hydrological Processes, Advances in Water Resources, Hydrological Sciences Journal, Journal of Hydrometeorology&lt;/em&gt;). Topic modeling uses statistical algorithms to extract semantic information from a collection of texts and has become an emerging quantitative method to assess substantial textual data. After acquiring all the papers published in the aforementioned journals and applying multiple pre-processing routines including removing punctuations, nonsensical texts, stopwords, and tokenizing, stemming, lemmatization etc., the resultant corpus was fed to the LDA model for &amp;#8216;learning&amp;#8217; latent intellectual topics. We achieved this using &lt;em&gt;Gensim&lt;/em&gt;, an open-source Python library widely used for unsupervised semantic modeling with LDA. The optimal number of topics (&lt;em&gt;k&lt;/em&gt;) and model hyperparameters were decided using coherence and perplexity values for multiple LDA models with varying &lt;em&gt;k&lt;/em&gt;.&amp;#160; The resulting generated topics are interpretable based on our prior knowledge of hydrology and related sub-disciplines. Comparative topic trend, term, and document level cluster analyses based on different time periods, journals and authors were performed. These analyses revealed topics such as climate change research gaining popularity in Hydrology over the last decade.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;We aim to use these results combined with probability distribution between topics, journals and authors to create an interactive ontology map that is useful for research scientists and environmental consultants for exploring relevant literature based on topics and topic relationships. The primary objective of this work is to allow science practitioners to explore new branches and connections in the Hydrology literature, and to facilitate comprehensive and inclusive literature reviews. Second-order beneficiaries are decision and policy makers: the proposed project will provide insights into current research trends and help identify transitions and argumentative viewpoints in hydrologic research. The outcomes of this project will also serve as tools to facilitate effective science communication and aid in bridging gaps between scientists and stakeholders of their research.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;

scholarly journals Categorising patient concerns using natural language processing techniques

2021 ◽  
Vol 28 (1) ◽  
pp. e100274
Author(s):  
Paul Fairie ◽  
Zilong Zhang ◽  
Adam G D'Souza ◽  
Tara Walsh ◽  
Hude Quan ◽  
...  
Keyword(s):  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Community Care ◽  
Topic Modelling ◽  
Text Data ◽  
Emergency Department Care ◽  
Patient Feedback ◽  
Care Facilities ◽  
Processing Techniques ◽  
Patient Concerns

ObjectivesPatient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the results of using a semiautomated, computational topic-modelling approach to analyse a corpus of patient feedback.MethodsPatient concerns were received by Alberta Health Services between 2011 and 2018 (n=76 163), regarding 806 care facilities in 163 municipalities, including hospitals, clinics, community care centres and retirement homes, in a province of 4.4 million. Their existing framework requires manual labelling of pre-defined categories. We applied an automated latent Dirichlet allocation (LDA)-based topic modelling algorithm to identify the topics present in these concerns, and thereby produce a framework-free categorisation.ResultsThe LDA model produced 40 topics which, following manual interpretation by researchers, were reduced to 28 coherent topics. The most frequent topics identified were communication issues causing delays (frequency: 10.58%), community care for elderly patients (8.82%), interactions with nurses (8.80%) and emergency department care (7.52%). Many patient concerns were categorised into multiple topics. Some were more specific versions of categories from the existing framework (eg, communication issues causing delays), while others were novel (eg, smoking in inappropriate settings).DiscussionLDA-generated topics were more nuanced than the manually labelled categories. For example, LDA found that concerns with community care were related to concerns about nursing for seniors, providing opportunities for insight and action.ConclusionOur findings outline the range of concerns patients share in a large health system and demonstrate the usefulness of using LDA to identify categories of patient concerns.

scholarly journals Topic Modeling and Sentiment Analysis of Electric Vehicles of Twitter Data

2021 ◽  
pp. 13-29
Author(s):  
H. P. Suresha ◽  
Krishna Kumar Tiwari
Keyword(s):  
Language Processing ◽  
Electric Vehicles ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Public Perceptions ◽  
Project Work ◽  
Common Location ◽  
Web App ◽  
Twitter Users ◽  
Social Media Tool

Twitter is a well-known social media tool for people to communicate their thoughts and feelings about products or services. In this project, I collect electric vehicles related user tweets from Twitter using Twitter API and analyze public perceptions and feelings regarding electric vehicles. After collecting the data, To begin with, as the first step, I built a pre-processed data model based on natural language processing (NLP) methods to select tweets. In the second step, I use topic modeling, word cloud, and EDA to examine several aspects of electric vehicles. By using Latent Dirichlet allocation, do Topic modeling to infer the various topics of electric vehicles. The topic modeling in this study was compared with LSA and LDA, and I found that LDA provides a better insight into topics, as well as better accuracy than LSA.In the third step, the “Valence Aware Dictionary (VADER)” and “sEntiment Reasoner (SONAR)” are used to analyze sentiment of electric vehicles, and its related tweets are either positive, negative, or neutral. In this project, I collected 45000 tweets from Twitter API, related hashtags, user location, and different topics of electric vehicles. Tesla is the top hashtag Twitter users tweeted while sharing tweets related to electric vehicles. Ekero Sweden is the most common location of users related to electric vehicles tweets. Tesla is the most common word in the tweets related to electric vehicles. Elon-musk is the common bi-gram found in the tweets related to electric vehicles. 47.1% of tweets are positive, 42.4% are neutral, and 10.5% are negative as per VADER Finally, I deploy this project work as a fully functional web app.

Sign in / Sign up

Export Citation Format

Share Document